Development of a New Wearable Device for the Characterization of Hand Tremor

Abstract

Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.

Keywords: inertial signals; machine learning; pattern prediction; tremor pattern; wearable device.

Figure 1

Examples of tremor activation patterns in electromyography (EMG) signals from antagonist muscles of the forearm: (A) alternating pattern, when flexor and extensor tremor bursts are phase-shifted; and (B) synchronous pattern, when flexor and extensor tremor bursts are in phase. Muscle bursts occur periodically; their repetition frequency is the characteristic frequency of tremor.

Figure 2

(a) Overall signal generation and processing system architecture: signals generated by RT-Ring are acquired by a mobile app and then sent to the backend server through a TCP connection over the mobile network. Processing output is then sent back to the mobile app. (b) Block diagram of the RT-Ring wearable device: the core of the device is represented by the ST Microelectronics LM6DSL 6-axis Inertial Measurement Unit (IMU) and by the ISP1807 module, incorporating a nRF52840 microcontroller, with 32 KHz and 32 MHz crystal clocks and BLE antenna. An MCP73831 charge controller provides charging current from USB interface to a 65 mAh Lithium-Polymer batter; an RT9193 Low Dropout regulator converts 3.7 V battery output to 1.8 V and delivers power to all other components. Power delivery is activated by a LTC2955 soft button controller. A MAX17048 digital fuel gauge accurately measures battery charge and communicates with the microcontroller.

Figure 3

Layout of the PCB: (a) Top layer, with (1) ISP-1807 module, (2) ST LSM6DSL IMU, (3) (optional) protection diodes for D0 and D1 USB lines, (4) LDO voltage regulator, and (5) battery charge controller; (b) bottom layer, with (1) USB socket, (2) pushbutton, (3) soft-button controller, and (4) fuel gauge; (c) assembled PCB with battery; (d) device worn on a patient’s middle finger. PCB: Printed Circuit Board; IMU: Inertial Measurement Unit; USB: Universal Serial Bus; LDO: Low Drop-Out.

Figure 4

Signals acquired by RT-Ring from two subjects with different tremor activation patterns: (a) raw accelerations, (b) raw angular velocities, (c) filtered accelerations, and (d) filtered angular velocities from a subject with alternating tremor pattern; (e) raw accelerations, (f) raw angular velocities, (g) filtered accelerations, and (h) filtered angular velocities from a subject with synchronous tremor pattern. Filtered signals have been processed using a 4th order, two-pass, zero-phase Butterworth filter.

Figure 5

Block diagrams of (a) preprocessing and Machine Learning process for the evaluation of optimal models; (b) processing of a single segment on the backend server.

Figure 6

Importance of optimal features subsets used for training Random Forest (a) and XGBoost (b) best models.

Figure 7

(a) ROC curve from Random Forest training with 5-fold 80–20% cross-validation, mean curve is plotted in green color, mean AUC is 0.91; (b) ROC curve from XGBoost training with 5-fold 80–20% cross-validation, mean curve is plotted in green color, mean AUC is 0.95; (c) calibration plots, assessing the agreement between observations and predictions by the two classifier models; (d) ROC curves evaluated on the testing set, showing AUC = 0.96 and AUC = 0.97 for RF and XGB, respectively.