A survey on wearable sensor modality centred human activity recognition in health care

By Yan Wang, Shuang Cang, Hongnian Yu. Expert systems with applications, 2019

374 citations recorded by the source corpus.

Abstract Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Demography

View Paper: https://www.semanticscholar.org/paper/59ec0e73bcf9e23c4fe3ba48ff1b8a98b342ac5c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Parisa Rashidi
2. Graziano Pravadelli
3. Azra Bihorac
4. Florenc Demrozi

In Computer Vision and Pattern Recognition

1. Parisa Rashidi
2. Graziano Pravadelli
3. Azra Bihorac
4. Florenc Demrozi
5. Diane Joyce Cook

In Demography

1. Parisa Rashidi

Citations in the corpus

• Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, background): “Y and colleagues [2] (2019) present the state-of-the-art sensormodalities in HARmainly focusing on the techniques associated with each step of HAR in terms of sensors, data preprocessing, feature learning, classification, activities, including both conventional and DL methods.”