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Elbow flexion and extension is a common rehabilitation routine that is widely performed by stroke patients to rehabilitate elbow joints. The biceps and triceps muscles are the responsible muscles for flexing and extending the elbow joint. Hence, analyzing the electrical activity of those muscles provides beneficial information on elbow motion intention and eventually can be used for controlling purposes of potential rehabilitation robots. We investigate the Electromyography (EMG) signals of the biceps and triceps of stroke patients and their roles in elbow flexion and extension. The investigation process involves collecting, processing, filtering, and segmenting the collected surface Electromyography (sEMG) signal to ultimately extract specific features. Then, the optimum feature for elbow motion prediction is identified to be later used for controlling purposes. Six time-domain features, specifically MAV, RMS, SD, SAV, SSC, and ZC, were chosen to evaluate their efficiency in predicting elbow joint motion. MAV, RMS, SD, and SAV are the features that showed similar behavior during elbow flexion and extension. However, SAV showed the highest variation in the magnitude when the muscle's state changed from contraction to relaxation and vice-versa. On the other hand, SSC and ZC features showed an arbitrary behavior, where no reliable results were achieved. Eight stroke patients participated in this study after obtaining the ethics approval and consent agreements. The clinical trials were conducted at the Department of Rehabilitation Medicine, Hospital Pengajar Universiti Putra Malaysia (HPUPM).

Elbow flexion and extension is a common rehabilitation routine that is widely performed by stroke patients to rehabilitate elbow joints. The biceps and triceps muscles are the responsible muscles for flexing and extending the elbow joint. Hence, analyzing the electrical activity of those muscles provides beneficial information on elbow motion intention and eventually can be used for controlling purposes of potential rehabilitation robots. We investigate the Electromyography (EMG) signals of the biceps and triceps of stroke patients and their roles in elbow flexion and extension. The investigation process involves collecting, processing, filtering, and segmenting the collected surface Electromyography (sEMG) signal to ultimately extract specific features. Then, the optimum feature for elbow motion prediction is identified to be later used for controlling purposes. Six time-domain features, specifically MAV, RMS, SD, SAV, SSC, and ZC, were chosen to evaluate their efficiency in predicting elbow joint motion. MAV, RMS, SD, and SAV are the features that showed similar behavior during elbow flexion and extension. However, SAV showed the highest variation in the magnitude when the muscle's state changed from contraction to relaxation and vice-versa. On the other hand, SSC and ZC features showed an arbitrary behavior, where no reliable results were achieved. Eight stroke patients participated in this study after obtaining the ethics approval and consent agreements. The clinical trials were conducted at the Department of Rehabilitation Medicine, Hospital Pengajar Universiti Putra Malaysia (HPUPM).

This paper investigates the performance of well known packet scheduling algorithms developed for single carrier wireless systems from a real time video streaming perspective. The performance evaluation is conducted using the downlink third generation partnership project long term evolution (3GPP LTE) system as the simulation platform. This paper contributes to the identification of a suitable packet scheduling algorithm for use in the downlink 3GPP LTE system supporting video streaming services. Simulation results show that, in the downlink 3GPP LTE system supporting video streaming services, maximum-largest weighted delay first (M-LWDF) algorithm outperforms other packet scheduling algorithms by providing a higher system throughput, supporting a higher number of users and guaranteeing fairness at a satisfactory level.

This paper investigates the performance of well known packet scheduling algorithms developed for single carrier wireless systems from a real time video streaming perspective. The performance evaluation is conducted using the downlink third generation partnership project long term evolution (3GPP LTE) system as the simulation platform. This paper contributes to the identification of a suitable packet scheduling algorithm for use in the downlink 3GPP LTE system supporting video streaming services. Simulation results show that, in the downlink 3GPP LTE system supporting video streaming services, maximum-largest weighted delay first (M-LWDF) algorithm outperforms other packet scheduling algorithms by providing a higher system throughput, supporting a higher number of users and guaranteeing fairness at a satisfactory level.