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.

Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification

“…Intelligent healthcare systems are capable of providing better patient satisfaction, helps health professionals with accurate, reliable, and real-time diagnosis, thus improving the healthcare facilities for DM patients. Intelligent systems using ML algorithms have now been widely researched for different biomedical systems and special importance is given to its application for disease diagnosis and minimization of health risks [ 21 , 22 , 23 , 24 , 44 , 45 , 46 , 47 , 48 ]. Alike other life-threatening diseases, DSPN has also caught the researchers’ attention for the development of an artificial intelligence-based diagnosing system for DSPN [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 48 ].…”

“…Recently, due to the enormous development in machine learning (ML) methods, their application in solving different disease classification problems are expanding [ 21 , 22 , 23 , 24 ]. Alike other diseases, development of an intelligent diagnosis system for DSPN have caught the interests of researchers because of the long term and severe complication arises due to DSPN.…”

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

“…To analyze this massive amount of multivariate time-series data, the conventional statistical approach is often inadequate, specifically in the context of making a diagnosis from the unseen data. In this case, machine-learning (ML) algorithms are beneficial and are being used to predict health events, intervention, and prevention [ 11 , 12 ]. In this review, we discuss the use of various ML algorithms for the analysis of data provided by wearable devices.…”

Design, Implementation, and Performance Evaluation of a Fiber Bragg Gratings (Fbg) Based Smart Insole to Measure Plantar Pressure and Temperature

“…The advancement of electronics and sensors has helped in the development of a more portable and reliable diagnostic wearable for the early detection of anomalies [1]. The emphasis on e-Health, especially during the pandemic period, has driven the need for self-diagnosis solutions (using sensor systems with machine learning solutions) and helps the medical staff in the early diagnosis of anomalies without compromising their daily routines [2][3][4].…”