Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Abstract

Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

SeAct: Semantic Adaptive Segmentation of Sensor Data Streams for Human Activity Recognition

“…Attention must be drawn to the fact that "atomic" actions themselves do not denote an activity like "eating": such recognition must analyze several actions. SeAct improves segmentation results by adopting a sensor-dependent window extension, a strategy lacking in most SOTA works [Serpush et al 2022]. Furthermore, we explore what we define as simple events, the information acquired by the sensors, processing it through complex events.…”

Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition

“…HAR can be defined as the method to analyse data from sensors and reconstruct the activity carried out by users based on the context in which they are placed [1]. The application of wearable sensors for HAR cover a wide area, including health monitoring [2], rehabilitation [3], fall detection [4], sport monitoring [5], and industrial applications [6]. Despite the success of HAR in multiple scenarios, the interest of the research community has been focused on the recognition of Activities of Daily Living (ADLs) [7,8], which are all those activities performed on a daily basis, ranging from functional mobility (often referred to as ambulation) to dressing, feeding, and maintaining personal hygiene.…”

A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

“…In remote areas where one cannot access the hospital services suffers a lot, and this leads to worsening the patient condition; this situation can be handled by new emerging technology, i.e., tiny wireless chips or sensors connected with IoT devices remotely monitors patient's health [ 4 ]. Wired or wireless sensors [ 5 ] connected to the patient's body acquire securely patient data and at the same time physicians have access to data; thus, it provides ease for decision making. IoT applications in the healthcare sector benefit everyone to access medical help remotely, enhance the duration of therapy, and affordable cost.…”