[ARTICLE] Upper Limb Rehabilitation System for Stroke Survivors Based on Multi-Modal Sensors and Machine Learning – Full Text PDF

ABSTRACT

Nowadays, rehabilitation training for stroke survivors is mainly completed under the guidance
of the physician. There are various treatment ways, however, most of them are affected by various factors
such as experience of physician and training intensity. The treatment effect cannot be fed back in time,
and objective evaluation data is lacking. In addition, the treatment method is complicated, costly, and highly
dependent on physicians. Moreover, stroke survivors’ compliance is poor, which leads to various limitations.
This paper combines the Internet-of-Things, machine learning, and intelligence system technologies to
design a smartphone-based intelligence system to help stroke survivors to improve upper limb rehabilitation.
With the built-in multi-modal sensors of the smart phone, training action data of users can be obtained,
and then transfer to the server through the Internet. This research presents a DTW-KNN joint algorithm
to recognize accuracy of rehabilitation actions and classify to multiple training completion levels. The
experimental results show that the DTW-KNN algorithm can evaluate the rehabilitation actions, the accuracy
rates of the classification in excellent, good, and normal are 85.7%, 66.7%, and 80% respectively. The
intelligence system presented in this paper can help stroke survivors to proceed rehabilitation training
independently and remotely, which reduces medical costs and psychological burden.

Full Text PDF

[Abstract] Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury.

Abstract

BACKGROUND:

Effective neurological rehabilitation requires long term assessment and treatment. The rapid progress of virtual reality-based assistive technologies and tele-rehabilitation has increased the potential for self-rehabilitation of various neurological injuries under clinical supervision.

OBJECTIVE:

The objective of this study was to develop a fuzzy inference mechanism for a smart mobile computing system designed to support in-home rehabilitation of patients with neurological injury in the hand by providing an objective means of self-assessment.

METHODS:

A commercially available tablet computer equipped with a Bluetooth motion sensor was integrated in a splint to obtain a smart assistive device for collecting hand motion data, including writing performance and the corresponding grasp force. A virtual reality game was also embedded in the smart splint to support hand rehabilitation. Quantitative data obtained during the rehabilitation process were modeled by fuzzy logic. Finally, the improvement in hand function was quantified with a fuzzy rule database of expert opinion and experience.

RESULTS:

Experiments in chronic stroke patients showed that the proposed system is applicable for supporting in-home hand rehabilitation.

CONCLUSIONS:

The proposed virtual reality system can be customized for specific therapeutic purposes. Commercial development of the system could immediately provide stroke patients with an effective in-home rehabilitation therapy for improving hand problems.

Source: Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury. – PubMed – NCBI