A review on applied machine learning in wearable technology and its applications

Randhawa, Princy; Shanthagiri, Vijay; Kumar, Ajay

doi:10.1109/iss1.2017.8389428

Cited by 22 publications

(16 citation statements)

References 25 publications

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“…56,57 Where ML has notable historical roots in the identification of abnormalities for cancer screening, the same approaches have been used to investigate useful markers from BioMeT data. 58,59 Once the challenges outlined above are successfully addressed, BioMeTs will facilitate establishing nuanced patient-level normative values for physiological measures, such as HR and blood glucose level, and behavioral measures, such as the estimated daily number of steps and sleep duration. This will 1) help with more accurate estimation of patient-level treatment interventions effects and allow for near real-time tracking of recovery or functional decline, and 2) allow the development of individualized clinical risk prediction models to identify subjects with an increased risk for adverse health-related events, such as falls in elderly, re-admissions in different clinical subgroups, or relapse in multiple sclerosis.…”

Section: Data Rights and Governancementioning

confidence: 99%

“…Such approaches are being adopted by major European academic studies that are currently underway to utilize real‐world, free‐living data from individuals with conditions, such as neurodegenerative movement disorders and immune‐mediated inflammatory diseases 56,57 . Where ML has notable historical roots in the identification of abnormalities for cancer screening, the same approaches have been used to investigate useful markers from BioMeT data 58,59 …”

Section: A Comparison Of Digitally Measured Biomarkers With Laboratormentioning

confidence: 99%

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Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

Godfrey

Vandendriessche

Bakker

et al. 2020

Clinical Translational Sci

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Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well-established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample-level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations. Measure what is measurable and make measurable what is not so. Galileo Galilei (1564-1642).

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Section: Data Rights and Governancementioning

confidence: 99%

Section: A Comparison Of Digitally Measured Biomarkers With Laboratormentioning

confidence: 99%

Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

Godfrey

Vandendriessche

Bakker

et al. 2020

Clinical Translational Sci

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“…In addition, Naive Bayes is a probabilistic technique performing data classification based on uppermost probability of its belonging to individual classes. Model and feature based ANN learn, recall and generalise from the given data through adjustment of weights that can perform checking and testing models for predicting final outcome [58]. Reinforcement learning, a promising advanced ML method, works as trial-and-error in analysing data and optimising sequential treatments particularly for chronic disease [29].…”

Section: ) Data Analysis Using MLmentioning

confidence: 99%

Development of Novel Big Data Analytics Framework for Smart Clothing

2020

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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.

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“…The jacket in this study is designed to constantly collect the data, analyze it and classify it with an interval of 500 ms. During the struggle for escape, the fabric which is worn by the subject undergoes stretching, bending, twisting of hand movements, etc. With this in mind, the right location for the sensors is chosen (Randhawa et al, 2017) such that the response from the stretching, pressure and movement is recorded without any delay or ambiguity.…”

Section: Introductionmentioning

confidence: 99%

Human activity detection using machine learning methods from wearable sensors

Randhawa

Shanthagiri

Kumar

et al. 2020

Self Cite

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Purpose The paper aims to develop a novel method for the classification of different physical activities of a human being, using fabric sensors. This method focuses mainly on classifying the physical activity between normal action and violent attack on a victim and verifies its validity. Design/methodology/approach The system is realized as a protective jacket that can be worn by the subject. Stretch sensors, pressure sensors and a 9 degree of freedom accelerometer are strategically woven on the jacket. The jacket has an internal bus system made of conductive fabric that connects the sensors to the Flora chip, which acts as the data acquisition unit for the data generated. Different activities such as still, standing up, walking, twist-jump-turn, dancing and violent action are performed. The jacket in this study is worn by a healthy subject. The main phases which describe the activity recognition method undertaken in this study are the placement of sensors, pre-processing of data and deploying machine learning models for classification. Findings The effectiveness of the method was validated in a controlled environment. Certain challenges are also faced in building the experimental setup for the collection of data from the hardware. The most tedious challenge is to collect the data without noise and error, created by voltage fluctuations when stretched. The results show that the support vector machine classifier can classify different activities and is able to differentiate normal action and violent attacks with an accuracy of 98.8%, which is superior to other methods and algorithms. Practical implications This study leads to an understanding of human physical movement under violent activity. The results show that data compared with normal physical motion, which includes even a form of dance is quite different from the data collected during violent physical motion. This jacket construction with woven sensors can capture every dimension of the physical motion adding features to the data on which the machine learning model will be built. Originality/value Unlike other studies, where sensors are placed on isolated parts of the body, in this study, the fabric sensors are woven into the fabric itself to collect the data and to achieve maximum accuracy instead of using isolated wearable sensors. This method, together with a fabric pressure and stretch sensors, can provide key data and accurate feedback information when the victim is being attacked or is in a normal state of action.

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A review on applied machine learning in wearable technology and its applications

Cited by 22 publications

References 25 publications

Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

Development of Novel Big Data Analytics Framework for Smart Clothing

Human activity detection using machine learning methods from wearable sensors

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