Learning Hand Kinematics for Parkinson's Disease Assessment Using a Multimodal Sensor Glove

Abstract

Hand dysfunctions in Parkinson's disease include rigidity, muscle weakness, and tremor, which can severely affect the patient's daily life. Herein, a multimodal sensor glove is developed for quantifying the severity of Parkinson's disease symptoms in patients' hands while assessing the hands' multifunctionality. Toward signal processing, various algorithms are used to quantify and analyze each signal: Exponentially Weighted Average algorithm and Kalman filter are used to filter out noise, normalization to process bending signals, K-Means Cluster Analysis to classify muscle strength grades, and Back Propagation Neural Network to identify and classify tremor signals with an accuracy of 95.83%. Given the compelling features, the flexibility, muscle strength, and stability assessed by the glove and the clinical observations are proved to be highly consistent with Kappa values of 0.833, 0.867, and 0.937, respectively. The intraclass correlation coefficients obtained by reliability evaluation experiments for the three assessments are greater than 0.9, indicating that the system is reliable. The glove can be applied to assist in formulating targeted rehabilitation treatments and improve hand recovery efficiency.

Keywords: Parkinson's disease; finger flexibility; hand muscle strength; hand stability; smart glove; wearable bioelectronics.

Figure 1

Multisensor‐based hand function assessment multimodal sensor glove. a,b) A depiction of the inner layering of the multimodal sensor glove and the layout of each sensor. Flexible film pressure sensors are located on the palmer side of the glove, with detection points corresponding to the palm of the hand, purlicue, middle phalanges, and distal phalanges. Flexible bending sensors are located on each of the dorsal phalanges. IMU and MCU are integrated onto the dorsal side of the glove. c) The 3D diagram of the main control circuit board, including the physical interfaces connected to MCU, IMU, bending sensors, and thin film pressure sensors. d) Hand function assessment process and HCI. The hand function assessment process includes collecting, processing, and analyzing hand kinematic signals.

Figure 2

Finger flexibility assessment. a) The process of the flexibility assessment. b) The finger bending angle of two subjects (patient and healthy subject) when completing nine assessment gestures (“Flat hand”, “Fist”, “OK”, “Orchid fingers”, “A”, “W”, “B”, “U”, “V”). c) Schematic diagram of the hand function assessment for 12 subjects. d) Heat map of hand flexibility assessment grades distribution given by the system and the doctor, respectively. The row lists grades given by the system, while the column lists grades given by the doctor.

Figure 3

Hand muscle strength assessment. a) The process of the muscle strength assessment. b) Four sets of customized assessment actions: i) grasp the cylinder, ii) pinch objects with fingertip, iii) grasp the ball, and iv) click objects with finger. c) The muscle strength status of 12 subjects completing customized actions. d) Interval partitioning results of cluster analysis. The ordinate represents grades, while the abscissa represents the range of the interval, given the specific values of the interval boundaries. e) Heat map of muscle strength assessment grades distribution given by the system and the doctor, respectively. The row lists grades given by the system, while the column lists grades given by the doctor.

Figure 4

Hand stability assessment. a) The process of the stability assessment. b) Comparison of different methods in terms of accuracy. c) Schematic diagram of the BPNN: nine input layer nodes, six hidden layer nodes, and three output layer nodes. d) Loss function of BPNN training. e) ROC curves for stability assessment based on BPNN. The horizontal coordinate of the ROC curve is not correlated with the vertical coordinate, and the closer the ROC curve is to the (0, 1) point, the better the model is represented. Area under curve (AUC) is the area enclosed by the ROC curve and the x‐axis. The value of AUC can be used to measure the goodness of the classifier, and the higher the AUC means the better the classification effect.