Trends in human activity recognition using smartphones

Abstract

Recognizing human activities and monitoring population behavior are fundamental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, and lifestyle and behavior tracking are some of the main applications that require the recognition of human activities. Over the past few decades, researchers have investigated techniques that can automatically recognize human activities. This line of research is commonly known as Human Activity Recognition (HAR). HAR involves many tasks: from signals acquisition to activity classification. The tasks involved are not simple and often require dedicated hardware, sophisticated engineering, and computational and statistical techniques for data preprocessing and analysis. Over the years, different techniques have been tested and different solutions have been proposed to achieve a classification process that provides reliable results. This survey presents the most recent solutions proposed for each task in the human activity classification process, that is, acquisition, preprocessing, data segmentation, feature extraction, and classification. Solutions are analyzed by emphasizing their strengths and weaknesses. For completeness, the survey also presents the metrics commonly used to evaluate the goodness of a classifier and the datasets of inertial signals from smartphones that are mostly used in the evaluation phase.

How Validation Methodology Influences Human Activity Recognition Mobile Systems

“…As a result, an information leak appears, artificially increasing the classifier’s accuracy. We can confirm this observation in several studies in the literature [ 7 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

“…Human Activity Recognition (HAR) is an emergent research area for autonomous and real-time monitoring of human activities and has been widely explored because of its good practical applications, such as behavior detection, ambient assisted living, elderly care, and rehabilitation [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Individuals express their routines through activities that are performed in particular situations and understanding these situations enables people to improve their daily lives.…”

Human Activity Recognition: A Comparative Study to Assess the Contribution Level of Accelerometer, ECG, and PPG Signals

“…A cross-subject model, on the other hand, is trained on multiple users and attempts to recognize the activity of a previously untrained user. This model tends to be more generic and is commonly used in practice, since cross-subject models are cheaper to train and easier to deploy [26,27].…”

Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications

“…As a general definition, HAR is the analysis and classification of signals related to human actions, and it can be divided in categories according to the type of devices and sensors employed to acquire those signals [16]. However, due to the wide diffusion of smart devices and to their ease of use with just minor installation constraints, wearable sensors have been attracting the most attention in HAR research over the last decade [17,18]. At the same time, from the algorithms standpoint, great efforts have been devoted to developing new machine learning (ML) models for more and more accurate classification, employing either traditional ML techniques or deep learning (DL) methods [19][20][21][22].…”