Fuzzy logic-based connected robot for home rehabilitation

Abstract

In this paper, a robotic system dedicated to remote wrist rehabilitation is proposed as an Internet of Things (IoT) application. The system offers patients home rehabilitation. Since the physiotherapist and the patient are on different sites, the system guarantees that the physiotherapist controls and supervises the rehabilitation process and that the patient repeats the same gestures made by the physiotherapist. A human-machine interface (HMI) has been developed to allow the physiotherapist to remotely control the robot and supervise the rehabilitation process. Based on a computer vision system, physiotherapist gestures are sent to the robot in the form of control instructions. Wrist range of motion (RoM), EMG signal, sensor current measurement, and streaming from the patient’s environment are returned to the control station. The various acquired data are displayed in the HMI and recorded in its database, which allows later monitoring of the patient’s progress. During the rehabilitation process, the developed system makes it possible to follow the muscle contraction thanks to an extraction of the Electromyography (EMG) signal as well as the patient’s resistance thanks to a feedback from a current sensor. Feature extraction algorithms are implemented to transform the EMG raw signal into a relevant data reflecting the muscle contraction. The solution incorporates a cascade fuzzy-based decision system to indicate the patient’s pain. As measurement safety, when the pain exceeds a certain threshold, the robot should stop the action even if the desired angle is not yet reached. Information on the patient, the evolution of his state of health and the activities followed, are all recorded, which makes it possible to provide an electronic health record. Experiments on 3 different subjects showed the effectiveness of the developed robotic solution.

Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review

“…The included studies ( n = 13) were published between 2011 and 2022 with the majority of the studies, 10 (76.9%), having been published between 2020-2022 25 , 27 , 56 – 63 which shows the shift in the use of VRehab and AI solutions across many populations to support rehabilitation of people living in the community. Among the included studies, 5 (38.5%) were conducted in the United States 25 , 57 , 62 – 64 , 2 (15.4%) in China 59 , 65 , 2 (15.4%) in Spain 56 , 58 , one (7.7%) in Greece 66 , one (7.7%) in Italy 61 , one (7.7%) in Tunisia 27 , and one (7.7%) in Ukraine 60 . Of the included studies, 12 (92.3%) were journal articles and 1 (7.7%) was a peer-reviewed conference publication.…”

“…The inclusion criteria specified the use of AI-driven VRehab platforms for patients or mixed (patients and healthy) populations; however, none of the included studies incorporated healthy individuals (along with patients). Six (46.2%) of the studies were stroke rehabilitation for acute and chronic stroke patients 25 , 57 – 59 , 64 , 65 , 5 (38.5%) of the studies were physical therapy rehabilitation for post-hip and knee-replacement surgeries 62 , 63 , wrist fracture 27 , polytrauma lower extremities 60 , and musculoskeletal injuries patients 56 , one (7.7%) of the studies were motor and cognitive rehabilitation for Rett syndrome patients 61 , and one (7.7%) focused on exercise-based cardiac rehabilitation for cardiovascular disease patients 66 . The age of participants in most of the studies was around 60 years old, involving late middle age and late adulthood, with only one study on Rett syndrome patients 61 and one on musculoskeletal injuries patients 56 involving early adulthood.…”

“…The age of participants in most of the studies was around 60 years old, involving late middle age and late adulthood, with only one study on Rett syndrome patients 61 and one on musculoskeletal injuries patients 56 involving early adulthood. Except for two single-sex studies 27 , 61 , all other studies recruited both sexes, with n = 139 (50.4%) females and n = 137 (49.6%) males across all the included studies.…”

“…The use of technology creates opportunities for Artificial Intelligence (AI) to be utilized in the VRehab setting 8 , 18 – 20 to address research questions involving assessment 21 , recognition 22 , 23 , and prediction 24 , 25 of various patient health outcomes. Applications of AI in VRehab include but are not limited to patient’s movement and physical activity analysis, physical exercise assessment 21 , pain detection and measurement 26 , 27 , affective state analysis 23 , and compliance prediction 24 .…”

A cascade fuzzy adaptive based interaction torque control of a pneumatically actuated forearm rehabilitation robot under disturbance effects

“…A different fuzzy logic control example was implemented by Huang et al 22 The patient improvement was evaluated from the range of motion point of view, but the human force/torque interaction dynamics were not considered. Bouteera et al 23 proposed an EMG-triggered fuzzy logic-based control for a wrist robot. The ability of fuzzy was discussed for only feature extraction (i.e.…”