In recent years, the emerging framework of the Internet of Things has been leading the technological landscape in a number of different fields and applications, from autonomous and connected vehicles to wearable devices. The healthcare system is benefiting from this continuously evolving environment since it leverages the opportunities offered by the ubiquitous and pervasive presence of connected objects and smart services. This attitude has given rise to the concept of eHealth, thus enabling new approaches and solutions for healthcare. In this framework we propose SmartPants, an IoT-based wireless system specifically designed for the remote rehabilitation of lower limbs in poststroke patients. The platform consists of multiple nodes used to monitor physical therapy and a software platform that provides real-time feedback on the execution by recognizing the type of exercise currently being performed by the patient. Our experimental results, evaluated through appropriate metrics, show that the proposed movement recognition algorithm provides very good results in terms of classification performance, independent of the considered classifier, with an average true positive rate of about 91 percent and an overall accuracy of around 96.5 percent.
via When eHealth Meets IoT: A Smart Wireless System for Post-Stroke Home Rehabilitation – IEEE Journals & Magazine
When stroke survivors perform rehabilitation exercises in clinical settings, experienced therapists can evaluate the associated quality of movements by observing only the initial part of the movement execution so that they can discourage therapeutically undesirable movements effectively and reinforce desirable ones as much as possible in the limited therapy time. This paper introduces a novel monitoring platform based on wearable technologies that can replicate the capability of skilled therapists. Specifically, we propose to deploy five wearable sensors on the trunk, and upper and forearm of the two upper limbs, analyze partial to complete observation data of reaching exercise movements, and employ supervised machine learning to estimate therapists’ evaluation of movement quality. Estimation performance was evaluated using F-Measure, Receiver Operating Characteristic Area, and Root Mean Square Error, showing that the proposed system can be trained to evaluate the movement quality of the entire exercise movement using as little as the initial 5s of the exercise performance. The proposed platform may help ensure high quality exercise performance and provide virtual feedback of experienced therapists during at-home rehabilitation.
Stroke is a leading cause of death and disabilities in adults, and the majority of its survivors suffer from upper extremity paresis . There is scientific evidence that repetitive rehabilitation exercises and training could improve motor abilities as a result of motor learning processes . Among many, a reaching movement is a fundamental component of daily movement that requires the coordination of multiple upper extremity segments . It is shown that repetitive reaching exercises improve the smoothness, precision, and speed of arm movements . To continue to improve and to sustain motor function, it is clinically important that patients continue to engage in rehabilitation exercises even outside the clinical settings , which emphasizes the importance of the home-based therapy.
via A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study – IEEE Conference Publication
In this paper, a novel walker robot is proposed for post-stroke gait rehabilitation. It consists of an omni-directional mobile platform which provides high mobility in horizontal motion, a linear motor that moves in vertical direction to support the body weight of a patient and a 6-axis force/torque sensor to measure interaction force/torque between the robot and patient. The proposed novel walker robot improves the mobility of pelvis so it can provide more natural gait patterns in rehabilitation. This paper analytically derives the kinematic and dynamic models of the novel walker robot. Simulation results are given to validate the proposed kinematic and dynamic models.
Stroke is one of the leading causes of death overall the world . According to a report from the American Heart Association, around 8 million population experience stroke onset every year worldwide . It remains many sequalae including a pathological walking pattern. Impaired walking function refrains stroke survivors from not only activities of daily living but also social participation, which causes poststroke depression in stroke survivors . Unfortunately, the depressed mood also negatively influences on the recovery of daily functions –. Moreover, decreased mobility is associated with other diseases such as obesity which leads to comorbidity then raise the possibility to get recurrent strokes , . This might become a vicious circle and form a huge economic burden for governments .
via Modelling and control of a novel walker robot for post-stroke gait rehabilitation – IEEE Conference Publication
Cloud-based rehabilitation services for post-stroke hand disability.
Tensor-based pattern recognition technique to detect the real-time condition of patient.
The integration of cloud computing with AR-based rehabilitation system.
Multi-sensory big data oriented tensor approach to handle patient’s collected data.
Given the flexibility and potential of cloud technologies, cloud-based rehabilitation frameworks have shown encouraging results as assistive tools for post-stroke disability rehabilitation exercises and treatment. To treat post-stroke disability, cloud-based rehabilitation offers great advantages over conventional, clinic-based rehabilitation, providing ubiquitous flexible rehabilitation services and storage while offering therapeutic feedback from a therapist in real-time during patients’ rehabilitative movements. With the development of sensory technologies, cloud computing technology integrated with Augmented Reality (AR) may make therapeutic exercises more enjoyable.
To achieve these objectives, this paper proposes a framework for cloud-based rehabilitation services, which uses AR technology along with other sensory technologies. We have designed a prototype of the framework that uses the mechanism of sensor gloves to recognize gestures, detecting the real-time condition of a patient doing rehabilitative exercises. This prototype framework is tested on twelve patients not using sensor gloves and on four patients wearing sensor gloves over six weeks. We found statistically significant differences between the forces exerted by patients’ fingers at week one compared to week six. Significant improvements in finger strength were found after six weeks of therapeutic rehabilitative exercises.
via Cloud-supported framework for patients in post-stroke disability rehabilitation
The growing importance of Kinect as a tool for clinical assessment and rehabilitation is due to its portability, low cost and markerless system for human motion capture. However, the accuracy of Kinect in measuring three-dimensional body joint center locations often fails to meet clinical standards of accuracy when compared to marker-based motion capture systems such as Vicon. The length of the body segment connecting any two joints, measured as the distance between three-dimensional Kinect skeleton joint coordinates, has been observed to vary with time. The orientation of the line connecting adjoining Kinect skeletal coordinates has also been seen to differ from the actual orientation of the physical body segment. Hence we have proposed an optimization method that utilizes Kinect Depth and RGB information to search for the joint center location that satisfies constraints on body segment length and as well as orientation. An experimental study have been carried out on ten healthy participants performing upper body range of motion exercises. The results report 72% reduction in body segment length variance and 2° improvement in Range of Motion (ROM) angle hence enabling to more accurate measurements for upper limb exercises.
Body joint movement analysis is extremely essential for health monitoring and treatment of patients with neurological disorders and stroke. Chronic hemiparesis of the upper extremity following a stroke causes major hand movement limitations. There is possibility of permanent reduction in muscle coactivation and corresponding joint torque patterns due to stroke . Several studies suggest that abnormal coupling of shoulder adductors with elbow extensors and shoulder abductors with elbow flexors often leads to some stereotypical movement characteristics exhibited by severe stroke patients . Therefore continuous and effective rehabilitation therapy is absolutely essential to monitor and control such abnormalities. There is a substantial need for home-based rehabilitation post-clinical therapy.
Source: Accurate upper body rehabilitation system using kinect – IEEE Xplore Document
Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user’s movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.
Stroke is a leading cause of adult disability around the world. A large number of stroke survivors are left with a unilateral arm or leg paralysis. After completing conventional rehabilitation therapy, a significant number of stroke survivors are left with limited reaching and grasping capabilities .
Source: Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient – IEEE Xplore Document
Treatment options for stroke survivors with severe hand impairment are limited. Active task practice can be restricted by difficulty in voluntarily activating finger muscles and interference from involuntary muscle excitation.
We developed a portable, actuated glove-orthosis, which could be employed to address both issues. We hypothesized that combining passive cyclical stretching (reducing motoneuronal hyperexcitability) imposed by the device with active-assisted, task-oriented training (rehabilitating muscle activation) would improve upper extremity motor control and task performance post-stroke.
Thirteen participants who experienced a stroke 2-6 months prior to enrollment completed 15 treatment sessions over five weeks. Each session involved cyclically stretching the long finger flexors (30 min) followed by active-assisted task-oriented movement practice (60 min). Outcome measures were completed at six intervals: three before and three after treatment initiation.
Overall improvement in post-training scores was observed across all outcome measures, including the Graded Wolf Motor Function Test, Action Research Arm Test, and grip and pinch strength ( p ≤ 0.02), except finger extension force. No significant change in spasticity was observed. Improvement in upper extremity capabilities is achievable for stroke survivors even with severe hand impairment through a novel intervention combining passive cyclical stretching and active-assisted task practice, a paradigm which could be readily incorporated into the clinic.
Source: IEEE Xplore Abstract – Use of a Portable Assistive Glove to Facilitate Rehabilitation in Stroke Survivors With Severe Hand …