In this paper, we propose and investigate a low-cost and low-complexity wireless ambulatory human locomotion tracking system that provides a high ranging accuracy (intersensor distance) suitable for the assessment of clinical gait analysis using wearable ultra wideband (UWB) transceivers. The system design and transceiver performance are presented in additive-white-gaussian noise and realistic channels, using industry accepted channel models for body area networks. The proposed system is theoretically capable of providing a ranging accuracy of 0.11 cm error at distances equivalent to interarker distances, at an 18 dB SNR in realistic on-body UWB channels. Based on real measurements, it provides the target ranging accuracy at an SNR = 20 dB. The achievable accuracy is ten times better than the accuracy reported in the literature for the intermarker-distance measurement. This makes it suitable for use in clinical gait analysis, and for the characterization and assessment of unstable mobility diseases, such as Parkinson's disease.

Assessment of human locomotion using wearable sensors is an efficient way of getting useful information about human health status, and determining human locomotion abnormalities. Wearable sensors do not only provide the opportunity to assess the behavior of patients as it happens in their daily life activities, but also provide quantitative, meaningful feedback data of patients to their therapists. This can pinpoint the cause of problems and help in maximizing their recovery rates. The popularity of using wearable sensors has received attention from a number of researchers from both the academic and industrial fields in the past few years. The different types of wearable sensors have given birth to the realization of a standard measurement model that can support different types of applications. Wireless body area networks (WBANs) are starting to replace traditional healthcare systems by enabling long-term monitoring of patients and tele-rehabilitation, especially those who suffer from chronic diseases. This paper investigates using wearable accelerometers and surface electromyography (EMG) in human locomotion monitoring for tele-rehabilitation. It proposes and investigates new positions for the proposed sensors, and compares the measured signals to similar techniques proposed in the literature. Realistic measurements show that the proposed positions of surface EMG sensors (on the forearm muscles) provide more reliable results in the classification of motion abnormality as compared to the sensor positions proposed in the literature (biceps muscles). Seven statistical features were extracted from accelerometer signals, and four time domain (TD) features are extracted from EMG signals. These features are used to construct six machine learning classifiers for automatic classification of Parkinson’s tremor. These models include; decision tree (DT), linear discriminant analysis analysis (LDA), k-nearest-neighbor (kNN), support vector machine (SVM), boosted tree and bagged tree classifiers. The performance of the applied classifiers is analyzed using accuracy, confusion matrix, and area under ROC (AUC) curve. The results are also compared to corresponding findings in the literature. The experimental results show that the highest classification accuracy is achieved when using the proposed measurement set and bagged tree classifier with a value of 99.6%.

Recent advancements in the "Internet of Things" allowed "Things" or "nodes" to have virtual identities and exchange data and services. "Things" form distributed systems which lack central control and have flexible topologies. In this context, it is crucial to have a robust, scalable and adaptive trust model for trustworthy node communication between nodes (i.e., Things). In this paper, A Context-based Social Trust model for the Internet of Things (CBSTM-IoT) is presented. CBSTM-IoT integrates numerous factors such as direct and indirect trust;transaction context; owner trust; and social modelling of trust. Performance evaluation results validate that CBSTM-IoT benefits from using both direct observations and indirect recommendations to counter attacks and achieves satisfactory results in isolating malicious nodes in the network.