An advanced scheme of compressed sensing of acceleration data for telemonintoring of human gait

Abstract

BackgroundThe compressed sensing (CS) of acceleration data has been drawing increasing attention in gait telemonitoring application. In such application, there still exist some challenging issues including high energy consumption of body-worn device for acceleration data acquisition and the poor reconstruction performance due to nonsparsity of acceleration data. Thus, the novel scheme of compressive sensing of acceleration data is needed urgently for solutions that are found to these issues.MethodsIn our scheme, the sparse binary matrix is firstly designed as an optimal measurement matrix only containing a smallest number of nonzero entries. And then the block sparse Bayesian learning (BSBL) algorithm is introduced to reconstruct acceleration data with high fidelity by exploiting block sparsity. Finally, some commonly used gait classification models such as multilayer perceptron (MLP), support vector machine (SVM) and KStar are applied to further validate the feasibility of our scheme for gait telemonitoring application.ResultsThe acceleration data were selected from open Human Activity Dataset of Southern California University (USC-HAD). The optimal sparse binary matrix (a smallest number of nonzero entries is 8) is as strong as the full optimal measurement matrix such as Gaussian random matrix. Moreover, BSBL algorithm significantly outperforms existing conventional CS reconstruction algorithms, and reaches the maximal signal-to-noise ratio value (70 dB). In comparison, MLP is best for gait classification, and it can classify upstairs and downstairs patterns with best accuracy of 95 % and seven gait patterns with maximal accuracy of 92 %, respectively.ConclusionsThese results show that sparse binary matrix and BSBL algorithm are feasibly applied in compressive sensing of acceleration data to achieve the perfect compression and reconstruction performance, which has a great potential for gait telemonitoring application.

DCS-based MBSBL joint reconstruction of multi-sensors data for energy-efficient telemonitoring of human activity

“…There possibly exists high-correlation information regarding human activity among several sensors located on body. 1,4 So, it is very vital to simultaneously process multi-sensors acceleration data for exploiting the inner-and inter-sensors correlation information associated with human activity. Theoretically, DCS technique has the powerful ability to simultaneously compress multi-sensors data, but it can produce the best joint reconstruction performance based on multi-sensors data that exist sparse enough.…”

“…In views of the fact that the dominant source of energy in WBANs is wasted during data transmission, most of previous studies only focused on regulating the data packet size to be sent for the design of wireless communication protocol with low energy consumption, and they do not consider to greatly reduce a larger amount of data during transmission for energy efficiency of sensor. 1,4 Over the past decade, 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. Although 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.…”

“…Although 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.…”

“…[1][2][3][4] In such applications, the multi-sensor nodes equipped with accelerometer are usually used to acquire the acceleration data simultaneously, and then, 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 Unfortunately, due to the limited energy of battery in each node, WBANs cannot continually collect the acceleration data during larger periods of time.…”

An Advanced Hybrid Technique of DCS and JSRC for Telemonitoring of Multi-Sensor Gait Pattern

“…The basic idea is that a larger amount of the acquired gait data is firstly compressed on sensors, in order to significantly reduce the energy consumption during data transmission. Then, all compressed data received in the remote terminal are perfectly reconstructed to perform further data processing [ 15 , 16 ]. For example, Wu et al investigated the application of CS for the energy-efficient telemonitoring of gait using acceleration data [ 16 ].…”