Development of a wearable ZigBee sensor system for upper limb rehabilitation robotics (original) (raw)

Upper-Extremity Stroke Therapy Task Discrimination Using Motion Sensors and Electromyography

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000

Brain injury resulting from stroke often causes upper-extremity motor deficits that limit activities of daily living. Several therapies being developed for motor rehabilitation after stroke focus on increasing time spent using the extremity to promote motor relearning. Providing a novel system for user-worn therapy may increase the amount and rate of functional motor recovery. A user-worn system comprising accelerometers, gyroscopes, and electromyography amplifiers was used to wirelessly transmit motion and muscle activity from normal and stroke subjects to a computer as they completed five upper-extremity rehabilitation tasks. An algorithm was developed to automatically detect the therapy task a subject performed based on the gyroscope and electromyography data. The system classified which task a subject was attempting to perform with greater than 80% accuracy despite the fact that those with severe impairment produced movements that did not resemble the goal tasks and were visually indistinguishable from different tasks. This developed system could potentially be used for home-therapy compliance monitoring, real-time patient feedback and to control therapy interventions.

Use of inertial sensors as devices for upper limb motor monitoring exercises for motor rehabilitation

Health and technology, 2015

This paper presents the development of a system that uses inertial sensors, wireless transceivers and virtual models to monitor the exercises of motor rehabilitation of the upper limbs based on Kabat's method. This method involves performing rehabilitation complex exercises that cannot be easily reproduced by the patient, requiring permanent assistance of a qualified professional. However, it is very expensive to have a professional expert assisting the patient throughout the treatment. Therefore, the development of technologies to monitor this type of exercise is necessary. The Kabat's method has several applications, e.g. in motor rehabilitation of stroke patients. Stroke is considered the second most common cardiovascular disorder and affects about 9.6 million people in Europe alone, and an estimated 6 million people worldwide die from this disorder. Also, the natural aging process increases the number of strokes, and the demand for healthcare and motor rehabilitation services. To minimize this problem, we propose an experimental system consisting of inertial sensors, wireless transceivers and virtual models according to the models of Denavit & Hartenberg and Euler Angles & Tait Bryan. Through inertial sensors, this system can characterize the movement performed by the patient, compare it with a predefined motion and then indicate if the motor system performed the correct movement. The patients monitor their own movements and the movement pattern (correct movement). All movements are stored in a database allowing continuous checking by a qualified professional. Several experimental tests have shown that the average system error was 0.97°, which is suitable to the proposed system.

Estimation of arm kinematics in stroke survivors using wearable sensors

2021

BackgroundStroke is the leading cause of long-term disability in the United States, often resulting in upper extremity (UE) motor impairment. Most existing outcome metrics of UE function in rehabilitation are insensitive to change or subject to observer bias. There is growing interest in using movement kinematics to measure UE motor function, since they can provide high-resolution, quantitative measurements. However, measuring arm kinematics in stroke survivors, particularly in the hospital or clinic, can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and a limited ability to perform required calibration poses. The aim of this study was to develop a general framework for accurate measurements of wrist position during reaching movements in people with stroke using relatively inexpensive wearable sensors.MethodsWe developed and presented two methods, one using inertial measurement units (IMUs) and using virtual reality (Vive) sensors, t...

Use of inertial sensors to measure upper limb motion: application in stroke rehabilitation

2010

Stroke is the largest cause of severe adult complex disability, caused when the blood supply to the brain is interrupted, either by a clot or a burst blood vessel. It is characterised by deficiencies in movement and balance, changes in sensation, impaired motor control and muscle tone, and bone deformity. Clinically applied stroke management relies heavily on the observational opinion of healthcare workers. Despite the proven validity of a few clinical outcome measures, they remain subjective and inconsistent, and suffer from a lack of standardisation. Motion capture of the upper limb has also been used in specialised laboratories to obtain accurate and objective information, and monitor progress in rehabilitation. However, it is unsuitable in environments that are accessible to stroke patients (for example at patients’ homes or stroke clubs), due to the high cost, special set-up and calibration requirements. The aim of this research project was to validate and assess the sensitivit...

Renovo: Prototype of a Low-Cost Sensor-Based Therapeutic System for Upper Limb Rehabilitation

Cornell University - arXiv, 2021

Stroke patients with Upper Limb Disability (ULD) are re-acclimated to their lost motor capability through therapeutic interventions, following assessment by Physiotherapists (PTs) using various qualitative assessment protocols. However, the assessments are often biased and prone to errors. Real-time visualization and quantitative analysis of various Performance Metrics (PMs) of patient's motion data, such as-Range of Motion (RoM), Repetition Rate (RR), Velocity (V), etc., may be vital for proper assessment. In this study, we present Renovo, a wearable inertial sensor-based therapeutic system, which assists PTs with real-time visualization and quantitative patient assessment, while providing patients with progress feedback. We showcase the results of a three-week pilot study on the rehabilitation of ULD patients (N=16), in 3 successive sessions at one-week interval, following evaluation both by Renovo and PTs (N=5). Results suggest that sensor-based quantitative assessment reduces the possibility of human error and bias, enhancing efficiency of rehabilitation. CCS Concepts: • Human-centered computing → Visualization systems and tools; Field studies; Gestural input; Graphical user interfaces; • Computer systems organization → Sensor networks; • Applied computing → Life and medical sciences.

Development of a Post-stroke Upper Limb Rehabilitation Wearable Sensor for Use in Sub-Saharan Africa: A Pilot Validation Study

Frontiers in Bioengineering and Biotechnology

The development of context-appropriate sensor technologies could alleviate the significant burden of stroke in Sub-Saharan African rehabilitation clinicians and health care facilities. However, many commercially available wearable sensors are beyond the financial capabilities of the majority of African persons. In this study, we evaluated the concurrent validity of a low-cost wearable sensor (i.e., the outREACH sensor) to measure upper limb movement kinematics of 31 healthy persons, using an 8-camera Vicon motion capture system as the reference standard. The outREACH sensor showed high correlation (r range: 0.808-0.990) and agreement (mean difference range: −1.60 to 1.10) with the reference system regardless of task or kinematic parameter. Moreover, Bland-Altman analyses indicated that there were no significant systematic errors present. This study indicates that upper limb movement kinematics can be accurately measured using the outREACH sensor, and have the potential to enhance stroke evaluation and rehabilitation in sub-Saharan Africa.

Assessment of Upper Limb Movement Impairments after Stroke Using Wearable Inertial Sensing

Sensors, 2020

Precise and objective assessments of upper limb movement quality after strokes in functional task conditions are an important prerequisite to improve understanding of the pathophysiology of movement deficits and to prove the effectiveness of interventions. Herein, a wearable inertial sensing system was used to capture movements from the fingers to the trunk in 10 chronic stroke subjects when performing reach-to-grasp activities with the affected and non-affected upper limb. It was investigated whether the factors, tested arm, object weight, and target height, affect the expressions of range of motion in trunk compensation and flexion-extension of the elbow, wrist, and finger during object displacement. The relationship between these metrics and clinically measured impairment was explored. Nine subjects were included in the analysis, as one had to be excluded due to defective data. The tested arm and target height showed strong effects on all metrics, while an increased object weight...

A novel motion tracking system for evaluation of functional rehabilitation of the upper limbs

Neural regeneration research, 2013

Upper limb function impairment is one of the most common sequelae of central nervous system injury, especially in stroke patients and when spinal cord injury produces tetraplegia. Conventional assessment methods cannot provide objective evaluation of patient performance and the tiveness of therapies. The most common assessment tools are based on rating scales, which are inefficient when measuring small changes and can yield subjective bias. In this study, we designed an inertial sensor-based monitoring system composed of five sensors to measure and analyze the complex movements of the upper limbs, which are common in activities of daily living. We developed a kinematic model with nine degrees of freedom to analyze upper limb and head movements in three dimensions. This system was then validated using a commercial optoelectronic system. These findings suggest that an inertial sensor-based motion tracking system can be used in patients who have upper limb impairment through data integ...

Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training

IEEE journal of translational engineering in health and medicine, 2018

High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a [Formula: see text]-statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an [Formula: see text]-scor...

Upper Limb Rehabilitation System for Stroke Survivors Based on Multi-Modal Sensors and Machine Learning

IEEE Access

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.