Compressed Sensing-Based IoMT Applications

Healthcare is becoming increasingly technologically advanced today, and the Internet of Things (IoT) is an integral part of personal healthcare systems. In a typical personal healthcare system, vital parameters are acquired from users and stored on a cloud platform for further analysis. Such systems analyze specific diseases through the acquisition of biomedical signals, including electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), and electrodermal activity (EDA). Wireless sensors are used to capture these physiological signals, which consume more power for signal acquisition and sampling, as well as large bandwidth during real-time data transmission. Therefore, real-time data compression is necessary to consume less power and channel bandwidth. Compressed sensing is a new paradigm that exploits signal sparsity and has attracted significant interest from researchers due to its ability to faithfully reconstruct signals from only a few measurements (less than the Nyquist sampling frequency). It also enables feature extraction directly from compressed measurements. This chapter provides the framework of compressed sensing for Internet of Medical Things (IoMT)-based healthcare devices, discussing various technical aspects of research components. A key aspect of this study is to investigate optimal sensing methods, reconstruction algorithms, and reconstruction-free approaches.