Sleep pattern and posture recognition have become of great interest for a diverse range of clinical applications. Autonomous and constant monitoring of sleep postures provides useful information for reducing the health risk. Prevailing systems are designed based on electrocardiograms, cameras, and pressure sensors, which are not only expensive but also intrusive in nature, and uncomfortable to use. We propose an unobtrusive and affordable smart system based on an electronic mat called Sleep Mat-e for monitoring the sleep activity and sleep posture of individuals living in residential care facilities. The system uses a pressure sensing mat constructed using piezo-resistive material to be placed on a mattress. The sensors detect the distribution of the body pressure on the mat during sleep and we use convolution neural network (CNN) to analyze collected data and recognize different sleeping postures. The system is capable of recognizing the four major postures—face-up, face-down, right lateral, and left lateral. A real-time feedback mechanism is also provided through an accompanying smartphone application for keeping a diary of the posture and send alert to the user in case there is a danger of falling from bed. It also produces synopses of postures and activities over a given duration of time. Finally, we conducted experiments to evaluate the accuracy of the prototype, and the proposed system achieved a classification accuracy of around 90%.

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Single-relay selection techniques based on the max-min criterion can achieve the highest bit error rate (BER) performance with full diversity gain as compared to the state-of-the-art single-relay selection techniques. Therefore, in this work, we propose a modified max-min criterion by considering the differences among the close value channels of all relays while selecting the best relay node. The proposed criterion not only enjoys full diversity gain but also offers a significant improvement in the achievable coding gain as compared to the conventional one. Basically, in this article, an improved bi-directional three-phase single-relay selection technique using the decodeand-forward protocol for wireless cooperative communication networks that enhances the overall network performance in terms of BER is proposed and its performance is proved analytically and through Monte-Carlo simulations. More specifically, the proposed criterion is first used to select the best relaynode. After that the selected relay-node forwards the information symbols of the communicating terminals after performing a digital network coding to minimize power consumptions. In our simulations, we show that our proposed technique outperforms the best-known single relay selection techniques. Furthermore, we prove that the BER results obtained from our conducted simulations perfectly match those obtained from the theoretical analysis.

Many coherent cooperative diversity techniques for wireless relay networks have recently been suggested to improve the overall system performance in terms of the achievable data rate or bit error rate (BER) with low decoding complexity and delay. However, these techniques require channel state information (CSI) at the transmitter side, at the receiver side, or at both sides. Therefore, due to the overhead associated with estimating CSI, distributed differential space-time coding techniques have been suggested to overcome this overhead by detecting the information symbols without requiring any (CSI) at any transmitting or receiving antenna. However, the latter techniques suffer from low performance in terms of BER as well as high latency and decoding complexity. In this paper, a distributed differential beamforming technique with power allocation is proposed to overcome all drawbacks associated with the later techniques without needing CSI at any antenna and to be used for cooperative communication networks. We prove through our analytical and simulation results that the proposed technique outperforms the state-of-the-art techniques in terms of BER with comparably low decoding complexity and latency.

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