Wearable Internet of Things - from human activity tracking to clinical integration

Kumari, Poonam; López-Benítez, Miguel; Lee, Gyu Myoung; Kim, Tae‐Seong; Minhas, Atul S.

doi:10.1109/embc.2017.8037330

Cited by 24 publications

(14 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, wearable sensors can overcome this problem. Kumari et al 26 proposed the WIoT architecture that interfaced the accelerometer, gyroscope, and magnetometer sensors with particle photon board. Using WiFi interface the particle photon board is connected to Raspberry Pi to collect the data and send it to ThingSpeak cloud for storage.…”

Section: Background and Related Workmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Background and Related Workmentioning

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

View full text Add to dashboard Cite

show abstract

“…As a result, within the IoT, including IIoT (industrial IoT [72]) and IoMT (internet of medical things [73,74]), the EC devices are extremely diverse, and the volume of data they are generating and processing is rapidly increasing. Data formats include time and frequency space signals, complex images, sound and voice, and a plethora of protected health [74,75,76], personal, and sensitive data. Due to the variety of EC devices, data types, and algorithms [77], many AI-based or smart offloading and transmission strategies are being proposed, such as employing machine and deep learning methods [75,78], or mimicking human brain networks [79].…”

Section: Edge Computingmentioning

confidence: 99%

“…Similarly, traffic lights augmented with hyperspectral imaging and chem-bio sensors can be made locally smart when combined with weather sensors and the unique local population and infrastructure signatures. More concrete EC applications include scalable framework for early fire detection [99], disaster management services [100], accelerometers for structural health monitoring [101], micro-seismic monitoring platform for hydraulic fracture [102], a framework for searchable personal health records [75,76,103], smart health monitoring [76,104] and healthcare framework [105], improved multimedia traffic [106], a field-programmable gate array (FPGA)-based system for cyber-physical systems [107] and for space applications [108], biomedical wearables for IoMT [73,76,109], air pollution monitoring systems [110], precision agriculture [111,112], diabetes [74] and ECG [109] devices, and marine sensor networks [113].…”

Section: Edge Computingmentioning

confidence: 99%

Nanosystems, Edge Computing, and the Next Generation Computing Systems

Passian

Imam

2019

Sensors

View full text Add to dashboard Cite

It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.

show abstract

“…Some researches on human activities, which works on offline recognition, are using machine learning tools such as WEKA [18][19][20]. Nowadays, some of clouding systems are being used for online recognition [21] [22].…”

Section: Offline Versus Online Har Systemsmentioning

confidence: 99%

Survey on Human Activity Recognition based on Acceleration Data

Slim¹,

Atia²,

Elfattah³

et al. 2019

ijacsa

View full text Add to dashboard Cite

Human activity recognition is an important area of machine learning research as it has many utilization in different areas such as sports training, security, entertainment, ambientassisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Therefore, the general architecture of HAR system is presented in this paper, along with the description of its main components. The state of the art in human activity recognition based on accelerometer is surveyed. According to this survey, Most of the researches recently used deep learning for recognizing HAR, but they focused on CNN even though there are other deep learning types achieved a satisfied accuracy. The paper displays a two-level taxonomy in accordance with machine learning approach (either traditional or deep learning) and the processing mode (either online or offline). Forty eight studies are compared in terms of recognition accuracy, classifier, activities types, and used devices. Finally, the paper concludes different challenges and issues online versus offline also using deep learning versus traditional machine learning for human activity recognition based on accelerometer sensors.

show abstract

Wearable Internet of Things - from human activity tracking to clinical integration

Cited by 24 publications

References 11 publications

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

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

Nanosystems, Edge Computing, and the Next Generation Computing Systems

Survey on Human Activity Recognition based on Acceleration Data

Contact Info

Product

Resources

About