A hybrid model for predicting human physical activity status from lifelogging data

Abstract

One trend in the recent healthcare transformations is people are encouraged to monitor and manage their health based on their daily diets and physical activity habits. However, much attention of the use of operational research and analytical models in healthcare has been paid to the systematic level such as country or regional policy making or organisational issues. This paper proposes a model concerned with healthcare analytics at the individual level, which can predict human physical activity status from sequential lifelogging data collected from wearable sensors. The model has a two-stage hybrid structure (in short, MOGP-HMM) -a multi-objective genetic programming (MOGP) algorithm in the first stage to reduce the dimensions of lifelogging data and a hidden Markov model (HMM) in the second stage for activity status prediction over time. It can be used as a decision support tool to provide real-time monitoring, statistical analysis and personalized advice to individuals, encouraging positive attitudes towards healthy lifestyles. We validate the model with the real data collected from a group of participants in the UK, and compare it with other popular two-stage hybrid models. Our experimental results show that the MOGP-HMM can achieve comparable performance. To the best of our knowledge, this is the very first study that uses the MOGP in the hybrid two-stage structure for individuals' activity status prediction. It fits seamlessly with the current trend in the UK healthcare transformation of patient empowerment as well as contributing to a strategic development for more efficient and cost-effective provision of healthcare.

Adjustment to a New Heart

“…This model includes the theoretical phase, fieldwork phase, and analytical phase. [ 16 ] The theoretical phase includes concept selection, review of literature, dealing with concept definition, and providing a definition. One of the most common methods for concept selection in a hybrid model of concept analysis is the consideration of clinical issues that nursing scholars deal with in teaching environments.…”

Enhancing operations management through smart sensors: measuring and improving well-being, interaction and performance of logistics workers

“…Exploiting innovative smart tools and a novel machine learning method, our study also represents an empirical contribution to the call for investigations on the implications of Big Data and innovative data science methods for management research (Dubey et al, 2019). Indeed, the application of novel approaches for organizational studies in real business settings, based on new tools (e.g., wearable sensors, social network platforms, smartphones) and methods of analysis (e.g., data mining, machine learning), is challenging and still quite limited albeit it is very promising (George et al, 2016;Chaffin et al, 2017;Ni et al, 2020). As shown by our results, such an analysis can provide new opportunities to evaluate neglected aspects of operations management (George et al, 2016).…”

“…However, the recent wide availability of wearable sensors and similar smart tools is providing researchers with the opportunity to collect and analyze data about human-behavior factors and work environment conditions, through effective data-driven methodologies (Chaffin et al, 2017;Ni et al, 2020;Stefanini et al, 2020). These new sensor-based tools (e.g., smartphones, smartwatches, and sociometric badges) allow researchers to quantitatively study the behaviors of logistics employees and their environmental conditions in deeper and more efficient ways, directly and without compromising their operational activities.…”

“…However, the recent wide availability of wearable sensors and similar smart tools is providing researchers with the opportunity to collect and analyze data about human-behavior factors and work environment conditions through effective data-driven methodologies (Chaffin et al. , 2017; Ni et al. , 2020; Stefanini et al.…”

AutoLog: Toward the Design of a Vehicular Lifelogging Framework for Capturing, Storing, and Visualizing LifeBits

“…The list of dedicated lifelogging devices comprises wearable devices, non-wearable devices, biometrics devices, fitness devices, and non-visual wearable devices. The wearable lifelogging devices are cameraenabled devices, such as SenseCam, OMG Autographer, head-mounted camera, and Go-Pro, etc., which passively capture images of what comes in front of a lifelogger and can be used for memory assistance, healthcare analytics, and lifestyle detection and analysis [4]. The biometric devices sense a lifelogger's body condition and collect information about skin temperature, galvanic skin response, and physiological responses such as heart rate and sympathetic nervous activity.…”