A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London

Abstract

The daily commute represents a source of chronic stress that is positively correlated with physiological consequences, including increased blood pressure, heart rate, fatigue, and other negative mental and physical health effects. The purpose of this research is to investigate and predict the physiological effects of commuting in Greater London on the human body based on machine-learning approaches. For each participant, the data were collected for five consecutive working days, before and after the commute, using non-invasive wearable biosensor technology. Multimodal behaviour, analysis and synthesis are the subjects of major efforts in computing field to realise the successful human–human and human–agent interactions, especially for developing future intuitive technologies. Current analysis approaches still focus on individuals, while we are considering methodologies addressing groups as a whole. This research paper employs a pool of machine-learning approaches to predict and analyse the effect of commuting objectively. Comprehensive experimentation has been carried out to choose the best algorithmic structure that suit the problem in question. The results from this study suggest that whether the commuting period was short or long, all objective bio-signals (heat rate and blood pressure) were higher post-commute than pre-commute. In addition, the results match both the subjective evaluation obtained from the Positive and Negative Affect Schedule and the proposed objective evaluation of this study in relation to the correlation between the effect of commuting on bio-signals. Our findings provide further support for shorter commutes and using the healthier or active modes of transportation.

Evaluating the Stressful Commutes Using Physiological Signals and Machine Learning Techniques

“…It makes people feel difficult to adapt to and balance both the internal and external environment [8]. EEG technology has been designed to monitor the stress level of people using brain signals [4]. The autonomic nervous system (ANS), which is made up of the antagonistically controlled sympathetic nervous system (SNS) and parasympathetic nervous system (PNS), is also known to be stimulated abnormally by stress [9].…”

“…An offline LabView-based model was designed to analyse the EEG signals to monitor the stress level [4]. That model used a 1-channel EEG headset to record EEG and an mobile application was used to capture the recorded EEG signals.…”

“…Electroencephalogram (EEG) helps in monitoring and measuring the various electrical processes in the human brain [4]. EEG signals are the representation of the electrical activities in the brain.…”

Wearable biosensors for human health: A bibliometric analysis from 2007 to 2022

“…Similarly, chronic stress has been studied by recording parameters such as blood pressure and heart rate during movements that people make on a working day, allowing the modification of habits for a healthier life. 16 Moreover, it has been studied by monitoring the autonomic nervous system using rings on the fingers. 17 …”

Frame-based System for Diagnosing Infertility in Males and Females

“…To address the aforementioned problems of infertility, this research aimed to develop an expert system for diagnosing infertility in both men and women. An expert system is a computer program designed to emulate human decision or thinking ability by learning automatically from given data to produce accurate outcome [10]. Looking at the stressful and time consuming manual processes faced by medical practitioners to diagnose patients, especially with the numerous challenges surrounding their line of work such as; similarities of symptoms shared among diseases, emergence of new diseases, little amount of time to diagnose each patient, few number of specialist available and the lack of interoperability between primary and specialty health care are factors that may contribute to medical errors [11].…”