Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Abstract

The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.

The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review

“…For example, an HMM algorithm was applied to classify the performed exercise into six possible correctness classes according to its execution [48]. Also, logistic regression was used for movement evaluation [34,49] and activity monitoring [50]. Logistic regression is considered the baseline supervised machine learning algorithm to classify an observation, and its outputs can be easily interpreted as probabilities of the occurrence of a class [71].…”

“…The sample was heterogeneous in terms of gender distribution and localization of impairments; limited numbers of raters for interrater agreements [34] Preliminary experimental study (lab)…”

“…Wearable systems are based on compact, lightweight sensors directly mounted on the interesting body part to analyze, sometimes resulting in embedded smart textile systems such as garments and gloves [28,34,37] or wrist-worn devices such as smart bands and smartwatches [32,40,49,52,53,57], or as the hardware part of immersive virtual reality systems (haptic interfaces and headset) [27]. The Inertial Measurement Unit (IMU) was the most common motion capture technology, thanks to its low cost and wearability.…”

“…IMUs combine linear acceleration from the accelerometer and the angular turning rates from gyroscopes resulting in accurate motion data. Moreover, IMUs coupled with different sensors such as optical fiber sensors [28] and flex and force resistive sensors [34] are useful to acquire more information about the amount of deflection or bending and the force applied during a rehabilitation task. Acceleration signals were also used coupled to electrocardiogram (ECG) data in order to better recognize the patient's physical activity form [56].…”

Middle East and North African Health Informatics Association (MENAHIA)

“…The capacity-building activities targeting physicians in digital health have extended to undergraduate medical students offered through a one credit hour introductory course that includes topics as coding, robotics, virtual reality (VR), telemedicine, and value-based care. Moreover, research in many domains of digital health has been published and under investigation in acceptance, adoption, efficiency, feasibility, and efficacy [12,13]. Many efforts have been targeted to funding opportunities and international partnerships allowing more smooth and solid kick starts and sustainable solutions.…”