Transducers have been used in the study of tremor for more than 100 years. Early investigators used a tambour and smoked drum to record physiologic and pathologic tremors.1 Accelerometers were introduced more than 50 years ago to elucidate tremor mechanisms and to quantify tremor.2 - 4 Early accelerometric studies required laboratory-based equipment that was bulky and expensive. These analog devices required a cable that connected the transducer to a bulky power supply, amplifier, and filters. The amplified signals were often stored on a magnetic tape recorder prior to digitization by an analog–digital converter for computer analysis. In the 1970s, the cost of an accelerometer ($1,000–$3,000), filters and amplifiers ($1,000–$3,000), software (>$1,000), analog–digital converters (>$1,000), and a computer (>$20,000) was prohibitive for most clinicians. The computer alone was the size of a small refrigerator!
The expense and size of transducers decreased dramatically with the advent of microelectromechanical systems (MEMS) technology in the 1990s. Small transducers are now integrated with microelectronic circuits for data acquisition and storage. Inertial measurement units (IMUs) contain a triaxial accelerometer, triaxial gyroscope, and frequently a triaxial magnetometer and altimeter, integrated with an electronic circuit for digital storage and wireless output.5 The cost of, and space required for, computing have plummeted with the development of portable laptop computers that have ample memory and computing power. Meanwhile, portable computer graphics tablets became available6,7 and are being used to quantify tremor in writing and drawing.8 - 12
The purpose of this review is to provide a practical overview of the use of portable motion transducers in the quantification of tremor. This is not a comprehensive review of all transducers that are currently being sold for the assessment of tremor; rather it is a practical guide to the selection and use of portable transducers in tremor analysis. MEMS technology has provided developers with an abundance of inexpensive IMUs that are being incorporated into a variety of devices that are potentially useful in the assessment of tremor. The list of such devices and applications is rapidly increasing and changing with advancing technology. Witness, for example, the Lift Pulse app for Android13 devices that uses the IMU in smart phones to measure tremor. Also consider the accelerometers and gyroscopes in wrist-worn activity monitors and smart watches.5,14 Those with sufficient range, sensitivity, and bandwidth are potentially useful for the measurement of tremor.
Accelerometers, gyroscopes, and digitizing tablets are considered in this review because they have been used most commonly to assess tremor in ambulatory settings. Their strengths, limitations, and methods of analysis are reviewed. Example software for tremor analysis is provided in an online appendix.
Medline was searched via PubMed.gov in December 2015 using the Boolean expression “tremor AND (accelerometer OR accelerometry OR gyroscope OR inertial measurement unit OR digitizing tablet OR transducer).” This search produced 419 papers dating back to 1964. The abstracts of these papers were reviewed for relevant portable transducers and mathematical methods of tremor analysis, and 105 papers written in English were reviewed in detail. Potentially relevant publications and references cited in these publications were also reviewed.
Motion of a body part (e.g., the hand) may consist of translational motion and rotational motion in three-dimensional space (Figure 1). In other words, a body part may exhibit any combination of anteroposterior, lateral, and vertical translation, and it can rotate about the anteroposterior (roll), lateral (pitch), and vertical (yaw) axes.
Tremor is an oscillatory motion that is roughly sinusoidal. Fourier’s theorem states that almost any continuous, periodic signal can be represented by the weighted sum of sines and cosines.15 The Fourier transform mathematically decomposes a signal into a series of sine and cosine waves. The amplitudes of the sines and cosines reveal the magnitudes of oscillation at the frequencies of motion. A plot of these amplitudes versus frequency is called an amplitude frequency spectrum. For example, if the signal is Asin(ωt), the amplitude frequency spectrum is a spike of magnitude A at frequency ω, and the so-called power (amplitude squared) spectrum is a spike of magnitude A2 at frequency ω. The mathematical details of these methods are described in many references on signal analysis.15
The fast Fourier transform (FFT) is a discrete Fourier transform that is computed with a mathematical algorithm that minimizes the number of computations, thus optimizing computational speed on a computer. This algorithm is implemented in many software packages. For example, Microsoft Excel provides an FFT that will accept signal recordings consisting of N data points, where N must be some integer power of 2 (e.g., 28 = 256), up to 4,096 (see online appendix). Other FFTs, such as those in MATLAB (www.mathworks.com) and Python (www.python.org), have fewer restrictions.
The frequency content of voluntary movement is generally concentrated at frequencies below 2 Hz, and most forms of tremor occur at 3 Hz or greater.16 An exception is myorhythmia, which is a rare form of slow tremor at <4 Hz.17 Voluntary movements are usually sufficiently arrhythmic that they do not produce a distinct spectral peak in the amplitude spectrum. Consequently, the frequency content of voluntary movement is usually distinguished easily from tremor.18,19 However, jerky voluntary and involuntary movements (e.g., chorea, myoclonus) contain high-frequency content that may extend into the frequency range of tremor. Tremor must be distinguished from these movements on the basis of rhythmicity, which usually results in a narrow spectral peak with a half-bandwidth of 2 Hz or less. Half-bandwidth is the width of the spectral peak at one-half the peak amplitude in the power spectrum or at 0.707 peak amplitude in the amplitude spectrum. Rhythmicity can also be quantified in terms of the variability in the period of successive tremor cycles.20,21 These methods are usually combined with high-pass filtering to separate low-frequency movement from tremor.20,21
Several factors are important when selecting a transducer for tremor analysis. The kinematic characteristics of tremor and motor task must be considered, and the transducer must have sufficient sensitivity, amplitude range, and frequency range to record tremor with good fidelity. The transducer also must be small enough and light enough to mount securely on the desired body part, without impeding the motor task. These issues are now discussed.
Translational displacement tremor T can be approximated by a sine wave with amplitude A in centimeters and frequency f in cycles per second (i.e., Hertz, Hz): T = Asin(2πft). Similarly, rotational tremor R can be expressed as R = θsin(2πft), where θ is the amplitude in degrees. Translational and angular velocities are the first derivatives of T and R: A(2πf)cos(2πft) and θ(2πf)cos(2πft), and translational and angular accelerations are the second derivatives of T and R: −A(2πf)2sin(2πft) and −θ(2πf)2sin(2πft). Therefore, if translational acceleration is measured, the amplitude of translational velocity is acceleration divided by 2πf, and translational displacement is acceleration divided by (2πf)2. Similarly, if angular velocity is measured, angular rotation is the amplitude of angular velocity divided by 2πf, and angular acceleration is angular velocity times 2πf. Accelerometers measure translational acceleration, and gyroscopes measure angular velocity. Digitizing tablets measure the translational displacement of a pen on the tablet surface.
Most tremors are primarily oscillatory rotation of a body part about a joint or multiple joints. For example, hand tremor may originate from tremulous rotation at the wrist, elbow, or shoulder. Similarly, head tremor is primarily rotation of the head about the neck. Consequently, a gyroscope mounted on the hand or head would seem ideal. However, until the advent of MEMS technology, gyroscopic transducers were too heavy and bulky for most tremor applications. These transducers are now very small and inexpensive.
Historically, accelerometers have been used more than other transducers in tremor analysis. Most accelerometers are piezoelectric, piezoresistive, or capacitive devices and have been incorporated into a variety of wearable devices.22,23 Capacitive MEMS accelerometers are now used most commonly in human motion analysis. These accelerometers record inertial acceleration and Earth’s gravitational force g (1 g = 9.8 m/second2). Inertial acceleration of a body part is a function of the force F applied to the mass m of the body part, according to Newton’s law F = ma. The effect of Earth’s gravity on an accelerometer is not constant when the accelerometer is rotating in space, and some rotational movement is virtually always present when recording tremor.24 Gravitational force will fluctuate between ±1 g as a single-axis accelerometer rotates in space. For example, if a single-axis accelerometer is rotated slowly at 1 Hz, the output recording will be a sinusoidal oscillation with frequency 1 Hz and amplitude 1 g. Accelerometers are designed to be sensitive to translation (linear acceleration), not angular rotation, so if an accelerometer is mounted precisely at the axis of rotation, its output will be entirely gravitational artifact. This gravitational artifact cannot be removed by high-pass filtering because it will be present at all frequencies of motion. Theoretically, multiple accelerometers, strategically mounted on a body part, can be used to estimate inertial acceleration, free of gravitational artifact.24,25 Note that translational inertial acceleration of ±1 g (= ±9.8 m/second2) at 1 Hz would require a peak-to-peak amplitude of displacement equal to 2·9.8/(2π1)2 = 0.496 m, so gravitational artifact can be considerable.
Gyroscopic transducers record angular velocity. Ideally, they are free of gravitational artifact, but all commercially available gyroscopes suitable for tremor analysis have some sensitivity to gravity and linear acceleration.26,27 This source of error is negligible unless angular velocity is integrated over time to compute angular rotation. Since tremor is roughly sinusoidal, its amplitude can be estimated by dividing angular velocity by 2πf, thus avoiding the cumulative error produced by numerically integrating a gyroscopic signal with drift and low-amplitude translational and gravitational acceleration artifact. Angular velocity is the same at any point on a rotating rigid body, so the location of a gyroscope is not critical, in contrast to accelerometry. The total angular velocity can be computed from a triaxial gyroscope as ω = (ωx2+ωy2+ωz2)0.5, where ωx is the angular velocity about the x-axis and so on.
Many commercially available motion sensors have a triaxial accelerometer and triaxial gyroscope housed in a single IMU, and a magnetometer and altimeter are also commonly included. Examples of such recording systems are presented in Table 1, and additional examples are the Shimmer3 (www.shimmersensing.com/) and Xsens IMUs (www.xsens.com/tags/imu/). IMUs have been designed for numerous applications, including automotive and aerospace sensors, personal electronic devices (smart phones, smart watches, tablets, computers, and game controllers), cameras, and wearable activity monitors. Many manufacturers (e.g., Fairchild, Analog Devices, InvenSense, Kionix, Colibrys, Motorola, Sensonor, Delphi) have produced an abundance of devices suitable for a wide range of applications (www.sens2b-sensors.com/directory/inertial-systems-motion/inertial-measurement-unit-imu). Each transducer has several important performance specifications, including number of axes, amplitude range, sensitivity, resolution, accuracy, bandwidth, noise, drift, mass, and size.5
|Sensor||Axes||Range||Mass||Resolution||Accuracy||Sampling Rate (samples/s)||Recording Duration|
|Accelerometer||3||±5 g||8.5 grams||12 bit||±2%||64||8 hr|
|Gyroscope||3||±2000 deg/s||±4%||64||8 hr|
|Accelerometer||3||±6 g or ±2 g||22 grams||14 bit||2%||20-128||12-24 hr**|
|Accelerometer||3||±16 g||14 grams||16 bit||3%||100||>24 hr|
|Wacom Intuos tablets||2||≥10 cm***||≥700 grams***||0.005 mm||±0.25 mm||≥100||As long as the pen is on the tablet|
The suitability of a transducer depends on the characteristics of tremor (amplitude and frequency) vis-à-vis the technical specifications of the transducer. The frequency of tremor must fall within the frequency range (bandwidth) of the transducer, and the largest anticipated tremor amplitudes should fall within the reported amplitude range of the transducer. For example, suppose the largest anticipated hand tremor amplitude is 30 cm peak to peak with a frequency of 4 Hz (a very severe tremor!). A good estimate of tremor acceleration is A(2πf)2, where A is half the peak-to-peak displacement amplitude and f is tremor frequency. Thus, our largest anticipated tremor acceleration is 9,478 cm/second2 or 9.67 g, so if we also account for the gravitational effect, the accelerometer should have a range of at least ±11 g. Similarly, for a 4 Hz 60-degree peak-to-peak severe rotational tremor, the maximum angular velocity is θ(2πf) = 754 degrees/second (θ = 30 degrees and f = 4 Hz), and the gyroscope should have a range of at least ±1,000 degrees/second.
Another important consideration is the resolution of the transducer. Until recently, most motion transducers were analog devices that produced a continuous voltage that was proportional to the physical property being measured (e.g., acceleration or angular velocity). The analog signal was sampled or digitized with a separate analog-to-digital converter, interfaced with a digital computer. The resolution of the analog-to-digital (A/D) converter was some power of 2. For example, 8-bit A/D conversion sampled the analog signal at 28 = 256 increments or levels, 12-bit A/D conversion at 4,096 levels, and 16-bit A/D conversion at 65,536 levels. Many transducers are now digital and either have built-in A/D converters or use pulse-width modulation to produce voltage pulses with a width that is proportional to acceleration or angular velocity. In all cases, the available increments of measurement are some power of 2 (e.g., 12 bit, 14 bit or higher). A 12-bit resolution produces output readings in 4,096 increments. Thus, if the range of the transducer is −6 g to +6 g, the resolution is 12 g/4,096 = 0.00293 g per increment or 2.87 cm/second2 per increment. This resolution is quite adequate for most pathologic tremors but is marginally adequate for physiologic hand (wrist) tremor, which has a mean baseline-to-peak (one-half peak-to-peak) amplitude range of 3 to 33 cm/second2 when the accelerometer is mounted 14 cm from the wrist (i.e., axis of rotation).28 Angular acceleration can be computed from these values using the equation α = a/r, where r is the distance of the accelerometer from the axis of rotation and a is the acceleration perpendicular to the axis of rotation (i.e., tangential to the curvilinear path of the accelerometer). Thus, for physiologic hand tremor, the mean amplitude range of angular acceleration is 0.214 to 2.36 radians/second2 or 12.3 to 135 degrees/second2 (2π radians = 360 degrees). Dividing these values by (2πf), where f = 8 Hz, gives the following estimates of angular velocity: 0.244 to 2.69 degrees/second. A gyroscope with range of ±2,000 degrees/second and 12-bit digitization has a resolution of 4,000/2,048 = 1.95 degrees/second, which is not adequate for physiologic hand tremor. Note, however, that this resolution can be improved by using a transducer with a lower amplitude range (e.g., ±2 g or ±1,000 degrees/second) or by selecting a transducer with more increments of digitization (e.g., 16 bit = 65,536 increments). Regardless of the resolution, the incremental value will have a small degree of inaccuracy, which is usually expressed as a percentage (e.g., ±3%) or as a range of the value (e.g., ±0.25 mm). The accuracy of digitizing tablets is considerably less than their digital resolution (Table 1).6
Tremor is measured when the body part is at rest (rest tremor), is voluntarily maintaining a constant posture (postural tremor), or is voluntarily moving (kinetic tremor). There is very little movement other than tremor during rest and constant posture, and any movement other than tremor will have frequency content below that of tremor. Therefore, the rhythmic oscillation of tremor is easily discernible in an amplitude or power spectrum. However, the frequency content of other voluntary or involuntary movements (e.g., chorea, myoclonus) may approach or overlap that of kinetic tremor if these movements are fast or jerky. The resulting spectrum will be a peak superimposed on a broad base of other spectral activity (Figure 2).
Gyroscopes and accelerometers accentuate tremor relative to lower-frequency voluntary movement because these devices record the first and second derivatives of rotation and displacement, respectively. Recall that the first derivative of a sinusoidal oscillation is a sinusoid multiplied by (2πf), and the second derivative is a sinusoid multiplied by (2πf)2. Thus, higher-frequency movement (i.e., tremor) is amplified relative to lower-frequency voluntary movement, as illustrated in Figure 3. However, other voluntary and involuntary movements will be amplified to the same extent as tremor if their frequency content is similar, and the tremor spectral peak in a frequency spectrum will be mixed with or partially obscured by other movement at the same frequency.
There are ways of mitigating the problems produced by rapid voluntary and involuntary movements. First, one can use voluntary tasks that are relatively slow, with no abrupt accelerations or decelerations. However, this approach might have little effect on other rapid involuntary movements, if they exist, and the slow voluntary movement may not be optimal for eliciting tremor. A sensor worn on the wrist during normal daily activities will record tremor and all sorts of voluntary and involuntary activity. For these recordings of spontaneous unconstrained activity, mathematical algorithms for identifying tremor must be employed, and such algorithms usually include the detection of rhythmic oscillation with spectral analysis or with some other algorithm.18 - 21,29
The body part being studied is another important consideration. Small body parts (e.g., the finger) require small recording devices. Many sensors for continuous monitoring of tremor are worn on the wrist like a watch, and in this location, the sensor will do a poor job of capturing tremor from rotation of the wrist and finger joints. The accelerometer in a smart phone can be programmed to record tremor, but mounting the smart phone on many body segments is difficult or impossible. The same is true for transducers housed in game controllers and other large electronic devices. Awkward or insecure mounting of a motion sensor on a body part will allow extraneous motion of the sensor and distortion of the tremor recording (i.e., motion artifact). In short, the mass, dimensions, and mounting of a motion sensor must be considered to ensure a valid recording of tremor.
Writing and drawing are favorite tasks in the assessment of upper-extremity action tremor, and digitizing tablets have sufficient resolution and accuracy to quantify tremor that is visible to the unaided eye.8 There are several manufacturers of digitizing tablets,30 but the Wacom Intuos tablets have been used almost exclusively in studies of tremor. Tablets are now portable and very affordable, and they have been used to identify pathologic tremor in genetic studies31 and to assess treatment effect in drug studies.9,11,12 Tablets lack sufficient sensitivity and accuracy to measure physiologic tremor. Consequently, drawings and writing by normal people will not produce a tremor spectral peak.32 This limitation is a strength when the goal is to identify people with abnormal tremor.31 Another limitation is that motion of the pen is not detected unless the pen tip is within 1 cm of the tablet surface, and patients with very severe tremor frequently cannot keep the pen so close to the tablet surface. Furthermore, tremor is recorded in only two dimensions and will not detect movement perpendicular to the tablet. Some pens have pressure sensors, but these sensors are nonlinear and must be calibrated. Consequently, these pressure sensors are useful in detecting tremor in pen pressure and movement of the pen from the tablet surface, but they are not useful in quantifying tremor amplitude. Finally, many tablets are sensitive to pen tilt, but this feature is not a necessity. In fact, pen-tilt sensitivity can be viewed as a source of artifact when measuring x–y motion of the pen tip, and pen-tilt sensitivity can be inactivated in many tablets.
The x–y displacements of the pen tip are transmitted digitally to a computer at 100 Hz or more. The x- and y-displacement data are numerically differentiated with frequency impulse response or Savistky–Golay differentiating filters33 to produce velocity and acceleration data that can be analyzed in a variety of ways, including Fourier analysis.6,7,9,32 Data transmission from tablet to computer requires the Wintab driver, which is supplied with all tablets. Writing data acquisition software with this driver requires considerable programming expertise. The Psychophysics toolbox version 3 (Psychtoolbox-3) for MATLAB is a free set of MATLAB programs for neuroscience research and contains MATLAB functions for interfacing tablets with MATLAB (http://psychtoolbox.org/download/). VBTablet (http://greenreaper.co.uk/#VBTablet) is a free Wintab 32-bit ActiveX component that has been used to record tablet data into 32-bit Microsoft Excel for analysis in MATLAB (see online appendix).11,12,34 There is also free tablet software for Archimedes spiral acquisition and analysis, written by Camilo Toro (http://www.neuroglyphics.org/).9
Tremor is an oscillatory movement, and all methods of analyzing recordings of tremor capitalize on this property. Here, we focus on Fourier spectral analysis because it is used most commonly. However, time-domain analyses20,21 and other frequency-domain analyses35–37 are also available. All of these methods are easily accomplished with MATLAB and its signal analysis toolbox (www.mathworks.com) or with Python and its SciPy library (www.scipy.org). Example software is provided in the online appendix.
Analog transducers produce a continuous voltage that is typically amplified, filtered, and then digitized with an A/D converter and computer. The sampling rate of the A/D converter must be at least twice the highest frequency in the transducer signal to avoid a phenomenon called aliasing, in which frequencies that are greater than half the sampling rate (i.e., Nyquist folding frequency) appear at lower “aliased” frequencies, according to the equation fa = |n·fs − f|, where fa is the aliased frequency, fs is the sampling rate, and n is the closest integer multiple of the sampling rate to the frequency f that is being aliased. For example, if the sampling rate is 100 Hz but there is noise at 60 Hz, the 60 Hz noise will appear at 40 Hz in the frequency spectrum. Aliasing can be avoided by using a sufficiently high sampling rate and by low-pass filtering the transducer signal to remove high-frequency noise. If the transducer is a digital device, then the user must make sure that the sampling rate is adequate and that the bandwidth of the transducer encompasses the frequency range of tremor and associated movement. Most portable transducers (IMUs) are now digital, but the digitizing frequency must still be at least twice the highest frequency in the transducer signal. Some activity monitors use sampling rates that are too low for tremor analysis.
The duration of the digitized recording will vary greatly with the application. Recording may be as short as a few seconds or may last hours or days. The frequency resolution of a frequency spectrum will be approximately 1/T, where T is the duration of the recording. Thus, the frequency resolution of a 2-second recording will be 0.5 Hz. If this is not adequate, longer recordings will be necessary. Longer recordings also may be necessary to obtain a representative sample of tremor, and the optimum recording duration will depend on the type of tremor and activity of the subject. Between 30 and 60 seconds is usually ample for postural tremor and for re-emergent rest tremor.38 Very mild rest tremor is typically very intermittent, so a 60-second recording with an augmenting task may be necessary.39 Continuous long-term recordings have also been used.21 Intention tremor may occur intermittently in certain phases of a task (e.g., as the finger approaches the nose), and 60 seconds or longer may be required to obtain representative samples. Longer recordings during prescribed activities such as holding a fixed posture or finger-to-nose testing may become impractical due to subject fatigue.
When a Fourier amplitude spectrum of a transducer recording is computed, the result is the root mean squared amplitude (0.707 peak amplitude) plotted versus time. This is appropriate when the tremor is more or less stable (statistically stationary) over time. However, many types of tremor are intermittent and are affected by a variety of external factors such as activity, stress, and medical interventions. Long-term recordings of intermittent tremor are more appropriately characterized with a time-frequency analysis that shows how the signal power and frequency change over time. The most common type of time-frequency analysis produces images called spectrograms (Figure 4). Spectrograms are created by performing spectral analysis on a series of "sliding windows." This approach is discussed by Wang and coworkers and is compared to another type of time-frequency analysis called the continuous wavelet transform, which is also known as a scaleogram.35 Example software is provided in the online appendix.
Short recordings or short segments of recordings should be multiplied by a so-called data window prior to spectral analysis to reduce a phenomenon known as spectral leakage. Spectral leakage causes spectral power from an oscillation to leak into neighboring frequencies surrounding the main spectral peak. This occurs when the frequency of the oscillation is not an integer multiple of the sampling rate divided by the number of data points in the segment being analyzed. Applying an appropriate data window (e.g., the Hanning window) reduces leakage.40 Additional information and example software are included in the online appendix.
Broad spectral peaks can be caused by random fluctuation in tremor frequency and inadequate spectral resolution, not just leakage. Spectral resolution of 0.2 Hz (5-second data segments) or 0.1 Hz (10-second data segments) is adequate for most types of tremor analysis. With this frequency resolution, broad tremor spectral peaks are often concluded to be due to fluctuation in tremor frequency. This conclusion can be corroborated with a time-frequency spectrogram (Figure 4).
It is important to recognize that all tremor recordings contain random variation in amplitude and frequency. In other words, tremor recordings are random processes or time series. Therefore, power spectra must be smoothed or averaged to obtain a spectral estimate that converges to the true spectrum of the process, without spurious spectral peaks. The most common approach is to divide a recording into L segments, compute the power spectrum of each segment, and then average the L power spectra. The variance of the power estimate at each frequency is thereby reduced by a factor of L, and the spectral estimate at each frequency is a chi-square random variable with 2L degrees of freedom.41 The 1-α confidence limits for the average spectral estimate X̂ is given in equation 1, where the spectral estimates have n = 2L degrees of freedom and χ2 is the chi-square value for n, α/2 and n, 1-α/2.15 Greater smoothing is achieved by analyzing overlapping segments, as described by Welch and used in the programs in the online appendix.41 Segment overlap of 50% results in a 33% increase in degrees of freedom, and 90% overlap produces nearly a 50% increase.42
The frequency resolution will be L/T Hz, where T is the duration of the recording.
Instead of spectral averaging, one may also "smooth" the spectrum of the entire recording by performing a weighted running average of sequential neighboring spectral values using a so-called spectral window, which is discussed elsewhere.43 With this method, the Fourier transform is calculated using the entire recording, instead of L segments. For long recording durations of 30 seconds or more, spectral leakage is negligible, so windowing the data series is not necessary. Bartlett’s method of smoothing M sequential frequencies in a power spectrum produces chi-square spectral estimates with n = 3M degrees of freedom, and the confidence limits can be computed with equation 1.15
It should now be apparent that these recording and analysis procedures are not rigidly defined, and the choice of instrumentation and analysis methods requires good judgment. Anatomical placement of the transducer, selection of motor task, duration of sampling, and methods of spectral analysis may vary among users. The best or optimum protocols have not been determined, and these will vary with the type of tremor being studied, the subject population, and the context. It is necessary to acknowledge this subjectivity in the design of studies and in the reporting of clinical and experimental results.
Transducers mounted appropriately have obvious face validity. However, face validity is reduced when the complexity of motion is not adequately considered. An accelerometer or gyroscope mounted on the hand will record hand tremor that is produced by a combination of wrist, elbow, and shoulder oscillations if the entire limb is permitted to move. Tremor at different joints may have different frequencies, producing multiple spectral peaks, and hand motion may be affected primarily by oscillation in proximal joints, not the wrist. Consequently, when interpreting a frequency spectrum, one cannot assume that a particular joint is involved unless motion was somehow restricted to that joint. These caveats are not an issue if the purpose is simply to measure the displacement or rotation of the hand in space, but they are an issue if wrist tremor, for example, is the specific interest.
Another consideration is that motion of one body part can be affected by motion elsewhere. For example, trunk tremor may be transmitted to the limbs and head, thereby reducing the validity of head and limb recordings. All of these limitations and caveats also pertain to clinical rating scales, to which portable transducers are frequently compared. Neither is a true gold standard. The accurate measurement of tremor generated by multiple joints can be accomplished only with multiple motion transducers or complex three-dimensional motion analysis systems,18,44 which are beyond the scope of this review and the capacity of most users.
Accelerometers, gyroscopes, and tablets have good convergent validity. However, the correlation between a transducer measure of tremor and a relevant rating scale is logarithmic, not linear.45,46 The logarithmic relationship stems from the fact that clinical ratings are nonlinear measures, as predicted by the Weber–Fechner law of psychophysics.45 This law states that the detectable (perceptible) change in a measure (e.g., tremor) is proportional to the initial value. Transducers, by contrast, are linear devices, and the detectable change is determined by their sensitivity (resolution and accuracy), not by the initial value. In general, a transducer measure of tremor T is related to the tremor rating TRS as follows: log T = α·TRS+β. The correlation between a transducer measure and TRS is very strong when these assessments are performed simultaneously (Figure 5). Correlations are less when the assessments are not performed simultaneously45 because tremor amplitude fluctuates greatly over time.47–49
The great precision and accuracy of transducers are mitigated by amplitude variability of tremor over time. This was learned in the pilot trial of the At Home Testing Device for quantitative assessment of Parkinson disease.50 In this study, patients performed weekly in-home assessments of tremor, reaction time, speech, and finger dexterity with a small portable system of transducers, including a wrist-worn accelerometer. None of these measures exceeded the sensitivity of the Unified Parkinson Disease Rating Scale motor score in detecting progression of disease over 6 months. This surprising result is largely explained by the large within-subjects variability of tremor and other motor functions. Change due to disease or treatment cannot be detected until it exceeds this natural variability. Statistically, this change is called minimum detectable change (MDC). MDC = SEM·1.96·√2, where the standard error of the measurement SEM is the square root of the within-subjects residual mean squared error (within-subjects variability) in a repeated-measures analysis of variance (ANOVA).51 Variability in tremor is by far the biggest source of test-retest variability, but variability in transducer mounting and analysis may also contribute. Regardless, the test-retest variability in tremor is so large that the MDC for transducers is comparable to that of a good rating scale,34,52 and the average of multiple measurements may be necessary to reduce variability.12 However, in some situations, transducers may still be preferable to rating scales because they enable tremor to be assessed more frequently with less cost, and they can be used almost anywhere, without clinician raters. Transducers may also be used to corroborate the results of clinical ratings.11,12
There is far more test-retest variability when tremor is measured continuously with a wearable transducer in an ambulatory setting because tremor and activity vary throughout the day. Nevertheless, continuous long-term recordings produce far more measurements of tremor, which can be averaged to reduce variability. One can also compute the times and percentage of time (occurrence) that a patient has tremor. The percentage of time with tremor is a valid measure of essential tremor and Parkinson rest tremor severity,21 and correlates with patient disability.53 The use of wearable transducers is still an active area of research and development. We do not yet know if continuous recordings of tremor can detect clinical change better than rating scales or transducers used in isolated assessments with prescribed activities.
The SEM and MDC of transducer data are computed with log-transformed data because transducer data are generally positively skewed and non-Gaussian. MDC is expressed as a percentage of the baseline geometric mean, and MDC% = (1−10−MDC) 100, where MDC is on log10 scale and MDC% is the percentage decrease in the geometric mean.54 When MDC is an increase, the formula for MDC% is (1−10MDC) 100. Experimentally determined MDCs have been no better than a 50% reduction or 200% increase in the baseline geometric mean.34,52,55 This is roughly equivalent to a one point change in a 0–4 tremor rating.45
Portable transducers for assessing tremor are now readily available at reasonable cost, and there is no question that they can provide valid measures of tremor amplitude, occurrence, and frequency. Transducers provide very precise linear measures of tremor, in contrast to the subjective, imprecise, nonlinear measures produced by clinical ratings. However, transducers have noteworthy limitations that must be considered. In the clinical assessment of tremor severity, the precision of transducers is largely mitigated by the inherent test-retest variability in tremor amplitude, resulting in minimum detectable change values that are comparable to those of clinical ratings.
The desirability of transducers versus scales depends on many factors, not just precision, and in the final analysis, the demands of an application will determine whether the cost and complexity of transducer recording and analysis are justified vis-à-vis the relative simplicity of a clinical rating scale. Transducers can be used to assess tremor without an experienced rater, can be used repeatedly with little additional cost, can be used in most locations, and can be used to corroborate the results of clinical ratings. Wearable transducers are capable of long continuous recordings, and such recordings are potentially helpful in monitoring medication response and disease progression in ambulatory settings. Transducers are necessary to measure tremor frequency accurately and to study spontaneous fluctuations in tremor amplitude, which may provide insight into underlying tremor mechanisms.56 We predict that transducers will become increasingly popular as standardized user-friendly analysis software is developed, including software that is web-based. Example software, written in MATLAB, is included in the online appendix.
The online appendix for this article is available here: http://dx.doi.org/10.7916/D8N879TT/.
APPENDIX.docx describes the contents of the appendix and how to install and use the programs.
FFT example.xlsm is a Microsoft Excel spreadsheet program containing macros for computing a power spectrum.
TremorSpectrum.m and TremorSpectrum.fig is a MATLAB program with graphical user interface for analyzing data from an inertial measurement unit. For readers who do not have MATLAB, standalone executable programs are provided for Windows and Mac OS X.
TabletSpectrum.m and TabletSpectrum.fig is a MATLAB program with graphical user interface for analyzing data from a digitizing tablet. For readers who do not have MATLAB, standalone executable programs are provided for Windows and Mac OS X.
Tablet.xls is a Microsoft Windows 32-bit Excel program for sampling data with a digitizing tablet. This will not work with 64-bit Excel.
Data files: ActiGraphdata.xlsx, finger nose no mass LUE.xls, artificialdata.csv, tabletdata.csv, artificial tablet data.csv
1 Funding: This work was supported by a grant from the Spastic Paralysis Research Foundation of Kiwanis International, Illinois-Eastern Iowa District.
2 Financial Disclosures: Rodger Elble received grant support from the Spastic Paralysis Research Foundation of Kiwanis International, Illinois-Eastern Iowa District. He received consulting fees from Sage Therapeutics and is a paid video rater for InSightec. James McNames is an employee of and owns stock in APDM, Inc.
3 Conflicts of Interest: R.E. has collaborated with Great Lakes NeuroTechnologies and APDM Wearable Technologies in the development of their motion transducers. J.M. and Portland State University have a financial interest in APDM, Inc., a company that may have a commercial interest in the results of this research and technology. This potential conflict has been reviewed and managed by Oregon Health & Science University.
4 Ethics statement: This study was performed in accordance with the ethical standards detailed in the Declaration of Helsinki. The authors' institutional ethics committee has approved this study and all patients have provided written informed consent.
Hsu, AW Piboolnurak, PA Floyd, AG et al. (2009). Spiral analysis in Niemann-Pick disease type C. Mov Disord 24: 1984–1990. doi: 10.1002/mds.22744. [PubMed]
Randall, JE and Stiles, RN (1964). Power spectral analysis of finger acceleration tremor. J Appl Physiol 19: 357–360. [PubMed]
Stiles, RN and Randall, JE (1967). Mechanical factors in human tremor frequency. J Appl Physiol 23: 324–330. [PubMed]
Morgan, MH, Hewer, RL and Cooper, R (1975). Intention tremor – a method of measurement. J Neurol Neurosurg Psychiatry 38: 253–258. doi: 10.1136/jnnp.38.3.253. [PubMed]
Rodríguez-Martín, D, Pérez-López, C, Samà, A, Cabestany, J and Català, A (2013). A wearable inertial measurement unit for long-term monitoring in the dependency care area. Sensors 13: 14079–14104. doi: 10.3390/s131014079. [PubMed]
Elble, RJ, Sinha, R and Higgins, C (1990). Quantification of tremor with a digitizing tablet. J Neurosci Methods 32: 193–198. doi: 10.1016/0165-0270(90)90140-B. [PubMed]
Pullman, SL (1998). Spiral analysis: a new technique for measuring tremor with a digitizing tablet. Mov Disord 13: 85–89. doi: 10.1002/mds.870131315. [PubMed]
Feys, P, Helsen, W, Prinsmel, A, Ilsbroukx, S, Wang, S and Liu, X (2007). Digitised spirography as an evaluation tool for intention tremor in multiple sclerosis. J Neurosci Methods 160: 309–316. doi: 10.1016/j.jneumeth.2006.09.019. [PubMed]
Haubenberger, D Kalowitz, D Nahab, FB et al. (2011). Validation of digital spiral analysis as outcome parameter for clinical trials in essential tremor. Mov Disord 26: 2073–2080. doi: 10.1002/mds.23808. [PubMed]
Haubenberger, D McCrossin, G Lungu, C et al. (2013). Octanoic acid in alcohol-responsive essential tremor: a randomized controlled study. Neurology 80: 933–940. doi: 10.1212/WNL.0b013e3182840c4f. [PubMed]
Zesiewicz, TA Sullivan, KL Hinson, V et al. (2013). Multisite, double-blind, randomized, controlled study of pregabalin for essential tremor. Mov Disord 28: 249–250. doi: 10.1002/mds.25264. [PubMed]
Elble, RJ, Lyons, KE and Pahwa, R (2007). Levetiracetam is not effective for essential tremor. Clin Neuropharmacol 30: 350–356. doi: 10.1097/WNF.0b013E31807A32C6. [PubMed]
Lift Pulse app for Android (). http://play.google.com/store/apps/details?id=com.liftlabsdesign.liftpulse.
Dai, H, Zhang, P and Lueth, TC (2015). Quantitative assessment of parkinsonian tremor based on an inertial measurement unit. Sensors 15: 25055–25071. doi: 10.3390/s151025055. [PubMed]
Bendat, JS and Piersol, AG (2010). Random data: analysis and measurement procedures. 4th ed Hoboken, NJ: Wiley.
Deuschl, G, Bain, P and Brin, M (1998). Consensus statement of the Movement Disorder Society on Tremor. Ad Hoc Scientific Committee. Mov Disord 13: 2–23. doi: 10.1002/mds.870131303. [PubMed]
Baizabal-Carvallo, JF, Cardoso, F and Jankovic, J (2015). Myorhythmia: phenomenology, etiology, and treatment. Mov Disord 30: 171–179. doi: 10.1002/mds.26093. [PubMed]
Lambrecht, S, Gallego, JA, Rocon, E and Pons, JL (2014). Automatic real-time monitoring and assessment of tremor parameters in the upper limb from orientation data. Front Neurosci 8: 221. doi: 10.3389/fnins.2014.00221. [PubMed]
Gallego, JA, Rocon, E, Roa, JO, Moreno, JC and Pons, JL (2010). Real-time estimation of pathological tremor parameters from gyroscope data. Sensors 10: 2129–2149. doi: 10.3390/s100302129. [PubMed]
Shaikh, AG Jinnah, HA Tripp, RM et al. (2008). Irregularity distinguishes limb tremor in cervical dystonia from essential tremor. J Neurol Neurosurg Psychiatry 79: 187–189. doi: 10.1136/jnnp.2007.131110. [PubMed]
van Someren, EJ, Pticek, MD, Speelman, JD, Schuurman, PR, Esselink, R and Swaab, DF (2006). New actigraph for long-term tremor recording. Mov Disord 21: 1136–1143. doi: 10.1002/mds.20900. [PubMed]
Yang, C-C and Hsu, Y-L (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10: 7772–7788. doi: 10.3390/s100807772. [PubMed]
John, D and Freedson, P (2012). ActiGraph and Actical physical activity monitors: a peek under the hood. Med Sci Sports Exerc 44: S86–S89. doi: 10.1249/MSS.0b013e3182399f5e. [PubMed]
Elble, RJ (2005). Gravitational artifact in accelerometric measurements of tremor. Clin Neurophysiol 116: 1638–1643. doi: 10.1016/j.clinph.2005.03.014. [PubMed]
Padgaonkar, AJ, Krieger, KW and King, AI (1975). Measurement of angular acceleration of a rigid body using linear accelerometers. J Appl Mech (Transactions ASME) 42: 552–556. doi: 10.1115/1.3423640.
Bancroft, JB and Lachapelle, G (2012). Estimating MEMS gyroscope G-sensitivity errors in foot mounted navigation. 2nd International Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Service. Helsinki: IEEE, pp. 1–6. http://plan.geomatics.ucalgary.ca/papers/upinlbs%202012_bancroft%202020etal_oct202012.pdf.
Weinberg, H (2011). Gyro mechanical performance: the most important parameter. Analog Devices, Technical Article MS-2158. http://www.analog.com/media/en/technical-documentation/technical-articles/MS-2158.pdf.
Elble, RJ (1986). Physiologic and essential tremor. Neurology 36: 225–231. doi: 10.1212/WNL.36.2.225. [PubMed]
Shaikh, AG, Zee, DS and Jinnah, HA (2015). Oscillatory head movements in cervical dystonia: dystonia, tremor, or both. Mov Disord 30: 834–842. doi: 10.1002/mds.26231. [PubMed]
Chie, TY (). Which graphics drawing tablet to buy in 2015 (non-display types), http://www.parkablogs.com/content/which-graphics-drawing-tablet-buy-2015-non-display-types2014.
Shatunov, A Sambuughin, N Jankovic, J et al. (2006). Genomewide scans in North American families reveal genetic linkage of essential tremor to a region on chromosome 6p23. Brain 129: 2318–2331. doi: 10.1093/brain/awl120. [PubMed]
Elble, RJ, Brilliant, M, Leffler, K and Higgins, C (1996). Quantification of essential tremor in writing and drawing. Mov Disord 11: 70–78. doi: 10.1002/mds.870110113. [PubMed]
Savitzky, A and Golay, MJE (1964). Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36: 1627–1639. doi: 10.1021/ac60214a047.
Akano, E, Zesiewicz, T and Elble, R (2015). Fahn-Tolosa-Marin scale, digitizing tablet and accelerometry have comparable minimum detectable change. Mov Disord 30: S556.
Wang, SY, Aziz, TZ, Stein, JF and Liu, X (2005). Time-frequency analysis of transient neuromuscular events: dynamic changes in activity of the subthalamic nucleus and forearm muscles related to the intermittent resting tremor. J Neurosci Methods 145: 151–158. doi: 10.1016/j.jneumeth.2004.12.009. [PubMed]
Torrence, C and Compo, GP (1998). A practical guide to wavelet analysis. Bull Am Meteorol Soc 79: 61–78. doi: 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2..
Ayache, SS, Al-Ani, T, Farhat, WH, Zouari, HG, Creange, A and Lefaucheur, JP (2015). Analysis of tremor in multiple sclerosis using Hilbert-Huang Transform. Neurophysiol Clin 45: 475–484. doi: 10.1016/j.neucli.2015.09.013. [PubMed]
Jankovic, J, Schwartz, KS and Ondo, W (1999). Re-emergent tremor of Parkinson’s disease. J Neurol Neurosurg Psychiatry 67: 646–650. doi: 10.1136/jnnp.67.5.646. [PubMed]
Cleeves, L, Findley, LJ and Gresty, M (1986). Assessment of rest tremor in Parkinson’s disease. Adv Neurol 45: 349–352. [PubMed]
Understanding FFTs windowing (2015). National Instruments, http://www.ni.com/white-paper/4844/en/.
Welch, PD (1967). The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust AU-15: 70–73. doi: 10.1109/TAU.1967.1161901.
Welch, PD (1987). On the relationship between batch means, overlapping batch means, and spectral estimation. Thesen, A, Grant, H and Kelton, WD eds. Proceedings of the 1987 Winter Simulation Conference. New York: ACM, pp. 320–323.
Jenkins, GM and Watts, DG (1968). Spectral analysis and its applications. San Francisco, CA: Holden-Day.
Rajaraman, V, Jack, D, Adamovich, SV, Hening, W, Sage, J and Poizner, H (2000). A novel quantitative method for 3D measurement of Parkinsonian tremor. Clin Neurophysiol 111: 338–343. doi: 10.1016/S1388-2457(99)00230-8. [PubMed]
Elble, RJ, Pullman, SL, Matsumoto, JY, Raethjen, J, Deuschl, G and Tintner, R (2006). Tremor amplitude is logarithmically related to 4- and 5-point tremor rating scales. Brain 129: 2660–2666. doi: 10.1093/brain/awl190. [PubMed]
Giuffrida, JP, Riley, DE, Maddux, BN and Heldman, DA (2009). Clinically deployable Kinesia technology for automated tremor assessment. Mov Disord 24: 723–730. doi: 10.1002/mds.22445. [PubMed]
Cleeves, L and Findley, LJ (1987). Variability in amplitude of untreated essential tremor. J Neurol Neurosurg Psychiatry 50: 704–708. doi: 10.1136/jnnp.50.6.704. [PubMed]
Elble, RJ, Higgins, C, Leffler, K and Hughes, L (1994). Factors influencing the amplitude and frequency of essential tremor. Mov Disord 9: 589–596. doi: 10.1002/mds.870090602.. Erratum in: Mov Disord 1995;10:411. [PubMed]
Pulliam, CL Eichenseer, SR Goetz, CG et al. (2014). Continuous in-home monitoring of essential tremor. Parkinsonism Relat Disord 20: 37–40. doi: 10.1016/j.parkreldis.2013.09.009. [PubMed]
Goetz, CG Stebbins, GT Wolff, D et al. (2009). Testing objective measures of motor impairment in early Parkinson’s disease: feasibility study of an at-home testing device. Mov Disord 24: 551–556. doi: 10.1002/mds.22379. [PubMed]
Weir, JP (2005). Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 19: 231–240. doi: 10.1519/00124278-200502000-00038. [PubMed]
Elble, R and Zesiewicz, T (2015). Fahn-Tolosa-Marin tremor scale and digitizing tablet have comparable minimum detectable change. Mov Disord 30: S558.
Louis, ED (2016). More time with tremor: the experience of essential tremor versus Parkinson’s disease patients. Mov Disord Clin Pract 3: 36–42. doi: 10.1002/mdc3.12207.
Spooner, J, Dressing, SA and Meals, DW (2011). Minimum detectable change analysis. Tech Notes 7, December 2011. Environmental Protection Agency, http://www.bae.ncsu.edu/programs/extension/wqg/319monitoring/tech_notes.htm: U.S.
Heldman, DA, Espay, AJ, LeWitt, PA and Giuffrida, JP (2014). Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson’s disease. Parkinsonism Relat Disord 20: 590–595. doi: 10.1016/j.parkreldis.2014.02.022. [PubMed]
Randall, JE (1973). A stochastic time series model for hand tremor. J Appl Physiol 34: 390–395. [PubMed]