Abstract
The joint reconstruction of nonsparse multi-sensors data with high quality is a challenging issue in human activity telemonitoring. In this study, we proposed a novel joint reconstruction algorithm combining distributed compressed sensing with multiple block sparse Bayesian learning. Its basic idea is that based on the joint sparsity model, the distributed compressed sensing technique is first applied to simultaneously compress the multi-sensors data for gaining the high-correlation information regarding activity as well as the energy efficiency of sensors, and then, the multiple block sparse Bayesian learning technique is employed to jointly recover nonsparse multi-sensors data with high fidelity by exploiting the joint block sparsity. The multi-sensors acceleration data from an open wearable action recognition database are selected to assess the practicality of our proposed technique. The sparse representation classification model is used to classify activity patterns using the jointly reconstructed data in order to further examine the effectiveness of our proposed method. The results showed that when compression rates are selected properly, our proposed technique can gain the best joint reconstruction performance as well as energy efficiency of sensors, which greatly contributes to the best sparse representation classification–based activity classification performance. This has a great potential for energy-efficient telemonitoring of human activity.
Highlights
The multi-sensor nodes equipped with accelerometer are usually used to acquire the acceleration data simultaneously, and the acquired data are transmitted via the Internet to the remote terminal for further data processing such as activity classification with high quality, in order to perfectly achieve the telemonitoring of human activity.[4,5]
The compressed sensing technique, an advanced methodology for data compression and reconstruction based on data sparsity, has been applied for energy efficiency of single-sensor, and its basic idea is that the collected data to be transmitted are significantly compressed on sensor node, and the compressed data received are reconstructed on remote terminal
The results demonstrated that our proposed distributed compressed sensing (DCS)-based MMV-based block sparse Bayesian learning (MBSBL) algorithm can jointly reconstruct nonsparse multi-sensors acceleration data with high fidelity, which helps further activity classification with high quality
Summary
Wireless body area networks (WBANs) that consist of wearable multi-sensor nodes have received wide attention in the field of telemonitoring of human activity because they have great advantages in practical applications such as remote diagnosis, realtime monitoring, and rehabilitation evaluation.[1,2,3,4] In such applications, the multi-sensor nodes equipped with accelerometer are usually used to acquire the acceleration data simultaneously, and the acquired data are transmitted via the Internet to the remote terminal for further data processing such as activity classification with high quality, in order to perfectly achieve the telemonitoring of human activity.[4,5] due to the limited energy of battery in each node, WBANs cannot continually collect the acceleration data during larger periods of time. The traditional compressed sensing technique can greatly decrease the energy consumption of single-sensor node during data transmission, it has no ability to jointly process the multi-sensor data for capturing the spatiotemporal correlation information associated with human activity.[1,4] Recently, the distributed compressed sensing (DCS) technique,[2,3] an emerging extension of CS framework for multi-signal case, has been successfully applied in many fields such as video coding, image fusion, and multichannel electrocardiograph monitoring. Its basic idea is to perform the joint compression and reconstruction of multi-sensors data on the assumption of the joint sparsity model.[5,6,7] Theoretically, DCS technique has a great potential for capturing the highcorrelation information from multi-sensors data, while the energy efficiency of sensors is potentially produced. It motivates us to find the effective techniques for jointly processing multi-sensors activity data
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