A ubiquitous system for smart reasoning for well-being at home and at work

Bosems, Steven

doi:10.1109/percomw.2012.6197560

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Physical activities are often instrumental in the enhancement of human physical and mental health. Their absence, on the other hand, can cause adverse effects on well-being such as obesity [ 1 ]. Specifically, they are influential during particular medical conditions such as gravidity, more commonly known as pregnancy.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors

Ullah

Iqbal

et al. 2021

Sensors

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We propose a physical activity recognition and monitoring framework based on wearable sensors during maternity. A physical activity can either create or prevent health issues during a given stage of pregnancy depending on its intensity. Thus, it becomes very important to provide continuous feedback by recognizing a physical activity and its intensity. However, such continuous monitoring is very challenging during the whole period of maternity. In addition, maintaining a record of each physical activity, and the time for which it was performed, is also a non-trivial task. We aim at such problems by first recognizing a physical activity via the data of wearable sensors that are put on various parts of body. We avoid the use of smartphones for such task due to the inconvenience caused by wearing it for activities such as “eating”. In our proposed framework, a module worn on body consists of three sensors: a 3-axis accelerometer, 3-axis gyroscope, and temperature sensor. The time-series data from these sensors are sent to a Raspberry-PI via Bluetooth Low Energy (BLE). Various statistical measures (features) of this data are then calculated and represented in features vectors. These feature vectors are then used to train a supervised machine learning algorithm called classifier for the recognition of physical activity from the sensors data. Based on such recognition, the proposed framework sends a message to the care-taker in case of unfavorable situation. We evaluated a number of well-known classifiers on various features developed from overlapped and non-overlapped window size of time-series data. Our novel dataset consists of 10 physical activities performed by 61 subjects at various stages of maternity. On the current dataset, we achieve the highest recognition rate of 89% which is encouraging for a monitoring and feedback system.

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Section: Introductionmentioning

confidence: 99%

A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors

Ullah

Iqbal

et al. 2021

Sensors

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“…Lack of physical activity can negatively affect physical fitness. 8 Recent studies show that the physical inactivity (aside from poor nutrition, smoking, and use of alcohol) is a significant cause of premature death. 9 This problem can be alleviated significantly with the help of physical activity tracking platforms.…”

Section: Introductionmentioning

confidence: 99%

Wearable Internet-of-Things platform for human activity recognition and health care

Iqbal

Ullah

Anwar

et al. 2020

International Journal of Distributed Sensor Networks

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We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.

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Intelligent assessment and prediction system for somatic fitness and healthcare using machine learning

Liu

Huang

et al. 2021

IFS

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This study proposes a health assessment and predictive assistance system for intelligent health monitoring. Through machine learning, the tool features a customized set of quantitative measurements and web analysis systems for physical and mental fitness. The system replaces the manpower and time requirements of the past necessary to conduct interviews and keep paper records, allowing users to observe and analyze physical and mental fitness status through the webpage. To achieve this, ECG, EEG, and EMAS are used to follow physiological, psychological, and meridian energy states. ASP.NET software is used as a development tool for the system cloud page, which constructs, documents, evaluates, and predicts functions for the smart health assistance system. The measurement data is entered and recorded in the cloud database. The data is used to construct an assessment and prediction of the user’s state of mind and body through machine learning methods, as well as the individual’s physical and mental fitness.

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A ubiquitous system for smart reasoning for well-being at home and at work

Cited by 3 publications

References 4 publications

A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors

A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors

Wearable Internet-of-Things platform for human activity recognition and health care

Intelligent assessment and prediction system for somatic fitness and healthcare using machine learning

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