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%.

This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors. Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications. We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB). Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly performs sensor fusion and feature selection. Being done jointly, the proposed pipeline empowers the learned model to benefit from the interaction of features that might have been dropped otherwise. Using statistical and time-based features from heterogeneous signals of the aforementioned sensor types, our approach attains a mean accuracy of 99.8%, which is the highest accuracy on HuGaDB in the literature. This research underlines the potential of incorporating EMG signals especially when fusion and selection are done simultaneously. Meanwhile, it is valid even with simple off-the-shelf feature selection methods such the Sequential Feature Selection family of algorithms. Moreover, through extensive simulations, we show that the left thigh is a key placement for attaining high accuracies. With one inertial sensor on that single placement alone, we were able to achieve a mean accuracy of 98.4%. The presented in-depth comparative analysis shows the influence that every sensor type, position, and placement can have on the attained recognition accuracies—a tool that can facilitate the development of robust systems, customized to specific scenarios and real-life applications.

A key research area in wireless transmission is underwater communications. It has a vital role in applications such as underwater sensor networks (UWSNs) and disaster detection. The underwater channel is very unique as compared to other alternatives of transmission channels. It is characterized by path loss, multipath fading, Doppler spread and ambient noise. Thus, the bit error rate (BER) is increased to a large extent when compared to its counterpart of cellular communications. Acoustic signals are the current best solution for underwater communications. The use of electromagnetic or optical waves obviously entails a much higher data rate. However, they suffer from high attenuation, absorption or scattering. This paper proposes a novel fractional fast Fourier transform (FrFT)—orthogonal frequency division multiplexing (FrFT-OFDM) system for underwater acoustic (UWA) communication—which employs the amplitude shift keying (ASK) modulation technique (FrFT-ASK-OFDM). Specifically, ASK achieves a better bandwidth efficiency as compared to other commonly used modulation techniques, such as quadrature amplitude modulation (QAM) and phase shift keying (PSK). In particular, the system proposed in this article can achieve a very promising BER performance, and can reach higher data rates when compared to other systems proposed in the literature. The BER performance of the proposed system is evaluated numerically, and is compared to the corresponding M-ary QAM system in the UWA channel for the same channel conditions. Moreover, the performance of the proposed system is compared to the conventional fast Fourier transform (FFT)-OFDM (FFT-OFDM) system in the absence and presence of the effect of carrier frequency offset (CFO). Numerical results show that the proposed system outperforms the conventional FFT-based systems for UWA channels, even in channels dominated by CFO. Moreover, the spectral efficiency and data rate of the proposed system are approximately double the values of the corresponding conventional OFDM systems for the same parameters.

This paper proposes a novel green radio (GR)-mismatched Rake receiver structure for ultra-wideband (UWB) communications in non-line-of-sight (NLOS) underground mine tunnels. The proposed architecture provides a promising solution for wireless GR communications in underground mine tunnels with greater than 90% reduction in power consumption. The bit error rate (BER) and maximum allowable data throughput (MADTh) of the proposed receiver structure are investigated via theoretical analysis as well as Monte-Carlo simulations in UWB NLOS underground mine tunnels and are compared to the corresponding optimal structures. The proposed mismatched receivers achieve an approaching MADTh and BER performance to the corresponding optimal structures with 1.5 dB BER performance degradation.