The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review

Abstract

Background: Physical activity (PA) is paramount for human health and well-being. However, there is a lack of information regarding the types of PA and the way they can exert an influence on functional and mental health as well as quality of life. Studies have measured and classified PA type in controlled conditions, but only provided limited insight into the validity of classifiers under real-life conditions. The advantage of utilizing the type dimension and the significance of real-life study designs for PA monitoring brought us to conduct a systematic literature review on PA type detection (PATD) under real-life conditions focused on three main criteria: methods for detecting PA types, using accelerometer data collected by portable devices, and real-life settings. Method: The search of the databases, Web of Science, Scopus, PsycINFO, and PubMed, identified 1,170 publications. After screening of titles, abstracts and full texts using the above selection criteria, 21 publications were included in this review. Results: This review is organized according to the three key elements constituting the PATD process using real-life datasets, including data collection, preprocessing, and PATD methods. Recommendations regarding these key elements are proposed, particularly regarding two important PA classes, i.e., posture and motion activities. Existing studies generally reported high to near-perfect classification accuracies. However, the data collection protocols and performance reporting schemes used varied significantly between studies, hindering a transparent performance comparison across methods. Conclusion: Generally, considerably less studies focused on PA types, compared to other measures of PA assessment, such as PA intensity, and even less focused on real-life settings. To reliably differentiate the basic postures and motion activities in real life, two 3D accelerometers (thigh and hip) sampling at 20 Hz were found to provide the minimal sensor configuration. Decision trees are the most common classifier used in practical applications with real-life data. Despite the significant progress made over the past year in assessing PA in real-life settings, it remains difficult, if not impossible, to compare the performance of the various proposed methods. Thus, there is an urgent need for labeled, fully documented, and openly available reference datasets including a common evaluation framework.

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection

“…This also provides useful guidance regarding the amount of time that people should spend on a specific activity type to maintain their health. Moreover, PA type is a more understandable concept than PA level, particularly for laypersons [5]. Thus, it is imperative to improve daily PA type detection to identify humans' daily PA patterns and their association with health outcomes.…”

“…Although the 3D accelerometer is the most common and informative sensor for PA type detection, it is challenging to accurately detect real-life activity types using only a single 3D accelerometer [5][6][7]. Researchers have extensively examined the usefulness of complementing accelerometer-based PA measures with additional sensors such as gyroscope, magnetometer, barometer and heart rate [8][9][10][11] or using multiple accelerometer devices on different body locations to improve the activity recognition [5,12]. However, these solutions entail mounting more devices on a person's body or rendering data analysis more complex due to dealing with different sensors featuring different data formats and sampling rates.…”

Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

“…Although these devices provide a privacy-aware alternative solution that overcomes many disadvantages of the external approach, they still might not be able to address the requirements of a diverse range of applications. A single wearable cannot cover the entire body and therefore fails to obtain adequate information about the mobility of all body segments [20][21][22] . For example, inertial sensors embedded in a smartwatch cannot capture the movement of legs, which restricts the capability of the system in classifying activities.…”

“…For example, inertial sensors embedded in a smartwatch cannot capture the movement of legs, which restricts the capability of the system in classifying activities. Additionally, in systems relying on data from a single device, variations in position can have a significant effect on the performance or lead to the failure of the monitoring system 20,23,24 .…”

Optimization and Validation of an Adjustable Activity Classification Algorithm for Assessment of Physical Behavior in Elderly

“…According to Preece et al the direct relation between the processing steps and thresholds in the decision tree on the one hand and the outcome measures on the other hand is one of the benefits of this classifier method [21]. Allahbakhshi et al state that this makes it easier to understand and interpret compared to other classifier methods [34], a prerequisite for an adjustable algorithm. To discriminate more specific categories of physical activity e.g., cycling or stair walking, a machine learning approach might be more suitable [35,36].…”