Development of Novel Big Data Analytics Framework for Smart Clothing

Abstract

Recent advances in micro electro-mechanical systems (MEMS) have produced wide variety of wearable sensors. Owing to their low cost, small size and interfacability, those MEMS based devices have become increasingly commonplace and part of daily life for many people. Large amount of data from heart and breath rates to electrocardiograph (ECG) signals, which contain a wealth of health-related information, can be measured. Hence, there is a timely need for novel interrogation and analysis methods for extracting health related features from such a Big Data. In this paper, the prospects from smart clothing such as wearable devices in generating Big Data are critically analyzed with a focus on applications related to healthcare, sports and fashion. The work also covers state-of-the-art data analytics methods and frameworks for health monitoring purposes. Subsequently, a novel data analytics framework that can provide accurate decision in both normal and emergency health situations is proposed. The proposed novel framework identifies and discusses sources of Big Data from the human body, data collection, communication, data storage, data analytics and decision making using artificial intelligence (AI) algorithms. The paper concludes by identifying challenges facing the integration of Big Data analytics with smart clothing. Recommendation for further development opportunities and directions for future work are also suggested. INDEX TERMSWearable technology, smart clothing, big data, data analytics, artificial intelligence (AI), machine learning (ML) and decision-making. He is currently a Postdoctoral Researcher with the Department of Engineering, Manchester Metropolitan University. He has participated in over three projects that delivered cost effective solutions for industry. His main research interests include developing hardware and software platforms, and algorithms for data analysis and cyber security purposes.ALHUSSEIN ALBARBAR is currently a Reader with the Department of Engineering, Manchester Metropolitan University. He has well over 27 years of industrial working experience and as an Academic Active Researcher. He led and participated in over $7M of major projects and supervised over 21 research degrees including 15 doctoral studies. He has published three books, five-book chapters, over 100 technical papers in refereed journals and international conference proceedings. His current research interests include Industry 4.0 applications, renewable power systems, smart sensing, intelligent control, and monitoring algorithms used for electromechanical power plants.

Smart Clothing Framework for Health Monitoring Applications

“…A mesh wireless network is responsible for communicating with each object and machine that are connected around the setup. The Wide Area Network (WAN) is responsible for many aspects of the technology, such as internet connection [46]. A summary of current wireless technologies mostly used for developing smart clothing is provided in Figure 7.…”

“…The collected raw data are stored in a local hardware-based system or cloud-based system and pre-processed through filtering (eliminating outlier) transforming, categorizing and turning it into a structured form such as an CSV or text file with the use of statistical techniques. An example of ECG data filtering can be found in the author's previous publication [46]. The accurate processing and management of physiological data allows health/fitness monitoring systems to make holistic assessments of patients, personalize treatments, and improve communication.…”

“…Physiological signals collected using smart clothing are noisy due to motion artefacts and thus misleading predictions of health condition. Appropriate methods [46] can be employed to filter the noisy monitored signal automatically at the time of data processing. Then, the filtered data can be used for assessing actual health and fitness conditions and taking appropriate actions.…”

“…Therefore, the development of reliable wearable sensors and a proper embedment between the sensor and clothing are required to avoid receiving any discontinuous signal. [46] can be employed to filter the noisy monitored signal automatically at the time of data processing. Then, the filtered data can be used for assessing actual health and fitness conditions and taking appropriate actions.…”

Assortment on the Bases of Big-Data Analytics: A Quantitative Analysis on Retail Industry

“…However, the best use of big-data analytics is on day-to-day operations (Oosthuizen et al, 2020), which is also not free from anomalies of relationships. Thus, companies must try to identify those situations where the communication looks most relevant (Chauhan et al, 2017) to relate communications with advanced algorithms to have desired and adequate results regarding customer preferences (Ahsan et al, 2020).…”