Machine Learning in Wearable Biomedical Systems

Abstract

Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems.