A Review of Wearable Internet-of-Things Device for Healthcare

Surantha, Nico; Atmaja, Prabadinata; Crowther, David; Wicaksono, M.Biomed. Arif

doi:10.1016/j.procs.2021.01.083

Cited by 52 publications

(20 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The behavior habit monitoring mainly used wearable devices to monitor the exercise, social and other living habits of young people, and help the subjects solve problems such as alcoholism and obesity. The psychological disorder monitoring mainly included anxiety related to mental health, communication disorder caused by loneliness, early depression, and neurological symptoms [ 5 ].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Detection and Application of Wearable Devices Based on Internet of Things in Human Physical Health

Lou¹,

Yang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In order to improve the detection function of wearable intelligent devices in the Internet of things and facilitate people to control a variety of information such as heart rate, exercise state, blood oxygen saturation, and so on, the scientific detection of human physical health based on wearable devices based on Internet of things technology is proposed. Through the combination of software- and hardware-related functional modules, the real-time detection of human physical health information can be effectively realized. Firstly, the detection principle of optical capacitance product pulse wave signal and the waveform characteristics of pulse wave are introduced, and then the application scenarios and advantages of wearable devices are further introduced; then, the convolutional neural network for pulse wave signal denoising and the basic principle of self-encoder are introduced; finally, the regression prediction method and support vector machine method for pulse wave signal feature extraction are introduced in detail. The pulse wave based on optical capacitance product is removed to improve the waveform quality of pulse wave signal. Firstly, the system software development environment is briefly described. Then, the software design of watch terminal master device based on MSP432 and belt terminal slave device based on MSP430 are described in detail, and the detailed program implementation flow of each key technology in the system is given. In addition, the fall detection algorithm based on threshold discrimination is studied, and the program implementation of the algorithm is also described in detail. Finally, the system is tested. The results show that normal state mainly include normal walking, jogging, and fast sitting, and the accuracy rate is 97%, 95%, and 93%, respectively. For fall state, the experimenter needs to simulate various possible fall states, and the accuracy rate is 95%, 93%, and 95%, respectively, which verifies the detection accuracy of the algorithm. The system can automatically turn on the satellite positioning function when the user’s physical sign parameters are abnormal or the user’s current fall dangerous situation occurs, and send the current position information and alarm content information through the GSM module, so that the dangerous situation can be found and handled at the first time.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…e psychological disorder monitoring mainly included anxiety related to mental health, communication disorder caused by loneliness, early depression, and neurological symptoms [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Detection and Application of Wearable Devices Based on Internet of Things in Human Physical Health

Lou¹,

Yang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The evolution and diffusion of the Internet of Things (IoT) paradigm and smart devices in recent years have resulted in the development of new healthcare, lifestyle analysis, and environmental protection solutions [1][2][3][4]. Wearable devices with various sensor types for gathering characteristics relevant to various quantifiable domains have drawn the interest of the scientific community and companies [5][6][7][8]. These technologies have been shown to be particularly effective for the continuous monitoring of a user's health as it is impacted by behavioral, physiological, psychological, and, most importantly, environmental parameters [9,10].…”

Section: Introductionmentioning

confidence: 99%

An Energy-Autonomous Smart Shirt Employing Wearable Sensors for Users’ Safety and Protection in Hazardous Workplaces

et al. 2022

View full text Add to dashboard Cite

Wearable devices represent a versatile technology in the IoT paradigm, enabling non-invasive and accurate data collection directly from the human body. This paper describes the development of a smart shirt to monitor working conditions in particularly dangerous workplaces. The wearable device integrates a wide set of sensors to locally acquire the user’s vital signs (e.g., heart rate, blood oxygenation, and temperature) and environmental parameters (e.g., the concentration of dangerous gas species and oxygen level). Electrochemical gas-monitoring modules were designed and integrated into the garment for acquiring the concentrations of CO, O2, CH2O, and H2S. The acquired data are wirelessly sent to a cloud platform (IBM Cloud), where they are displayed, processed, and stored. A mobile application was deployed to gather data from the wearable devices and forward them toward the cloud application, enabling the system to operate in areas where a WiFi hotspot is not available. Additionally, the smart shirt comprises a multisource harvesting section to scavenge energy from light, body heat, and limb movements. Indeed, the wearable device integrates several harvesters (thin-film solar panels, thermoelectric generators (TEGs), and piezoelectric transducers), a low-power conditioning section, and a 380 mAh LiPo battery to accumulate the recovered charge. Field tests indicated that the harvesting section could provide up to 216 mW mean power, fully covering the power requirements (P¯ = 1.86 mW) of the sensing, processing, and communication sections in all considered conditions (3.54 mW in the worst-case scenario). However, the 380 mAh LiPo battery guarantees about a 16-day lifetime in the complete absence of energy contributions from the harvesting section.

show abstract

“…IoT mixes the strengths of electronics and networking. Sensors and communication modules are distributed in a network and across multiple networks [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Software in the IoT equipment watches and controls the data traffic, resource allocation and choice of a network and conversion to different routes.…”

Section: Introductionmentioning

confidence: 99%

A Chip Antenna for Bluetooth Earphones with Cross-Head Interference Tested from Received-Signal Sensing

Seo

Cho

Lee

et al. 2022

Sensors

View full text Add to dashboard Cite

In this paper, a novel chip antenna and its function in wireless connectivity are presented for Bluetooth (BLT) earphones. The chip antenna is a metamaterial so compact (<λ/8), as the size of 4.9 × 13.0 × 2.0 mm3, that when it is mounted on the realistic PCB, it can be held in the enclosure of the BLT earphone. This setting does not degrade the resonance (S11 < −10 dB) of the proposed antenna. As two earphones in a pair are demanded to communicate with each other, one shares an RF signal with the other and they take turns as the master and slave. The received signal sensing is conducted with the latest model of human head-ear-phantom located between the earphones to mimic the real use-case and cross-head interference. Electromagnetic simulation of the antenna is done and verified by fabrication and measurement. Particularly, received-signal strength indications between the proposed antennas in the earphones are experimentally obtained as −67.5 dBm and −70 dBm without and with the head-ear-phantom, respectively, much greater than −120 dBm, the limit of detection, and implying acceptable connectivity and invulnerability over cross-head-interference problems.

show abstract

A Review of Wearable Internet-of-Things Device for Healthcare

Cited by 52 publications

References 29 publications

Detection and Application of Wearable Devices Based on Internet of Things in Human Physical Health

Detection and Application of Wearable Devices Based on Internet of Things in Human Physical Health

An Energy-Autonomous Smart Shirt Employing Wearable Sensors for Users’ Safety and Protection in Hazardous Workplaces

A Chip Antenna for Bluetooth Earphones with Cross-Head Interference Tested from Received-Signal Sensing

Contact Info

Product

Resources

About