[Abstract] Implementing technology enhanced real-time action observation therapy in persons with chronic stroke: A pilot study

ABSTRACT

This pilot study examined a novel technology-enhanced real-time action observation therapy (TERTAOT) of symmetrical bilateral movements in survivors of chronic stroke regardless of their ability to move their paretic limb(s). The TERTAOT used a Kinect XBox One to project mirror images of non-paretic limbs as participants performed symmetrical bilateral motor tasks involving whole-body movements in sitting or standing. The participants received eight weeks of treatment consisting of 30-minutes of conventional physical therapy (balance training, gait training, neuromuscular reeducation, and generalized strength training) and 30-minutes of the TERTAOT protocol per session (three sessions per week for a total of 24 sessions). Ten Meter Walk Test (10MWT), Five Times Sit-to-Stand (5TSTS), Timed Up and Go (TUG), Motor Activity Log – Quality of Movement (QOM) and Amount of Use (AOU) were administered at baseline (pretest), 4 weeks (posttest 1) and 8 weeks (posttest 2) post-TERTAOT, and 3 months after TERTAOT ended (retention). A General Linear Model Repeated Measures (parametric test) or the Friedman Test (non-parametric test) was used to compare outcomes across time points, depending on the normality of data distribution. Bonferroni post-hoc corrections were applied. Seventeen participants completed >80% of TERTAOT sessions without adverse events. The effect of time was significant for 10MWT (p = .001), 5TSTS (p = .001), TUG (p = .005), QOM (p = .001), and AOU (p = .017). TERTAOT may be feasible to be implemented in an outpatient setting. Improvements in functional outcomes including gait, balance, and use of upper limbs were observed after eight weeks of conventional therapy and TERTAOT protocol in survivors of chronic stroke.

Source

[Abstract + References] Arm Games for Virtual Reality Based Post-stroke Rehabilitation – Conference paper

Abstract

Stroke is a leading cause of serious long-term disability. World Health Organization (WHO) published that the second leading of death is stroke accident and every year, 15 million people worldwide suffer from stroke attack, two-thirds of them have a permanent disability. Muscle impairment can be treated by intensive movements involving repetitive task, task-oriented and task-variegated. Conventional stroke rehabilitation is expensive, less engaging and at the same time need more time for the rehabilitation process and need more energy and time for the therapist to guide the stroke-survivor. Modern stroke rehabilitation is more promising and more effective with modern rehabilitation aids allowing the rehabilitation process to be faster, however, this therapist method can be obtained in the big cities. To cover the lack of rehabilitation process in this research will develop and improve post-stroke rehabilitation using games. This research using electromyography (EMG) device to analyze the muscle contraction during the rehabilitation process and using Kinect XBOX to record trajectory hands movements. Five games from movements sequence have designed and will be examined in this research. This games obtained two results, the first is the EMG signal and the second is trajectory data. EMG signal can recognize muscle contractions during playing game and the trajectory data can save the pattern of movements and showed the pattern to the monitor. EMG signal processing using time or frequency feature extractions is a good idea to obtain more information from muscle contractions, also velocity, similarities and error movements can be obtained by study the possible approaches.

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via Arm Games for Virtual Reality Based Post-stroke Rehabilitation | SpringerLink

[Abstract] An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation

Abstract:

Rehabiliation robotics combined with video game technology provides a means of assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The current work presents ReHabGame, a serious game using a fusion of implemented technologies that can be easily used by patients and therapists to assess and enhance sensorimotor performance and also increase the activities in the daily lives of patients. The game allows a player to control avatar movements through a Kinect Xbox, Myo armband and rudder foot pedal, and involves a series of reach-grasp-collect tasks whose difficulty levels are learnt by a fuzzy interface. The orientation, angular velocity, head and spine tilts and other data generated by the player are monitored and saved, whilst the task completion is calculated by solving an inverse kinematics algorithm which orientates the upper limb joints of the avatar. The different values in upper body quantities of movement provide fuzzy input from which crisp output is determined and used to generate an appropriate subsequent rehabilitation game level. The system can thus provide personalised, autonomously-learnt rehabilitation programmes for patients with neuromuscular disorders with superior predictions to guide the development of improved clinical protocols compared to traditional theraputic activities.

Published in: Industrial Electronics (ISIE), 2017 IEEE 26th International Symposium on

Source: An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation – IEEE Xplore Document