Challenges in the design of self-powered wearable wireless sensors for healthcare Internet-of-Things

Lian, Yong

doi:10.1109/asicon.2015.7517022

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…Due to the major requirements of device's miniaturization (compact and lightweight) and affordability, wearables or portable electronic devices are constrained with limited resources including the battery power due to tiny size, processing capabilities, on-broad memory and limited data rate [19]- [21]. Despite these limited resources, wearable health monitoring devices must enable continuous monitoring for long period of time.…”

Section: A Importance Of Data Compression For Energy-constrained Devicesmentioning

confidence: 99%

“…For improving the energy-efficiency of battery-operated wearable devices, on-device or edge-computing has become more crucial and also increasingly intelligent and important part in minimizing the energy consumption of the data transmission or storage in order to maximize lifetime of a device as compared to cloud-based health monitoring solutions [10], [19]- [26]. One of the approach to reduce transmission or/and storage energy consumption is the data compression by using a lightweight compression method [16], [17], [18].…”

Section: A Importance Of Data Compression For Energy-constrained Devicesmentioning

confidence: 99%

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Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices

2022

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Real-time photoplethysmogram (PPG) denoising and data compression has become most essential requirements for accurately measuring vital parameters and efficient data transmission but that may introduce different kinds of waveform distortions due to the lossy processing techniques. Subjective quality assessment tests are the most reliable way to assess the quality, but they are time expensive and also cannot be incorporated with quality-driven compression mechanism. Thus, finding a best objective distortion measure is highly demanded for automatically evaluating quality of reconstructed PPG signal that must be subjectively meaningful and simple. In this paper, we present four types of objective distortion measures and evaluate their performance in terms of quality prediction accuracy, Pearson correlation coefficient and computational time. The performance evaluation is performed on different kinds of PPG waveform distortions introduced by the predictive coding, compressed sampling, discrete cosine transform and discrete wavelet transform. On the normal and abnormal PPG signals taken from five standard databases, evaluation results showed that different subjective quality evaluation groups (5-point, 3-point and 2-point rating scale) had different best objective distortion measures in terms of prediction accuracy and Pearson correlation coefficient. Moreover, selection of a best objective distortion measure depends upon type of PPG features that need to be preserved in the reconstructed signal.

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Section: A Importance Of Data Compression For Energy-constrained Devicesmentioning

confidence: 99%

Section: A Importance Of Data Compression For Energy-constrained Devicesmentioning

confidence: 99%

Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices

2022

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“…With regard toenvironmental sensors, these are mainly used to measure temperature, humidity and displacement [ 3 ], as well asthe Smart Citizen platform [ 4 ], whichincludes CO and NO 2 gas sensors for automotive applications. Other sensors are commonly used for healthcare applications to record Electrocardiography (ECG), Electroencephalogram (EEG) and Electromyogram (EMG) data [ 5 ]. For industrial applications, there are no frequently-used gas sensors [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Development of the MOOSY4 eNose IoT for Sulphur-Based VOC Water Pollution Detection

Climent

Pelegrí-Sebastiá

Sogorb

et al. 2017

Sensors

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In this paper, we describe a new low-cost and portable electronic nose instrument, the Multisensory Odor Olfactory System MOOSY4. This prototype is based on only four metal oxide semiconductor (MOS) gas sensors suitable for IoT technology. The system architecture consists of four stages: data acquisition, data storage, data processing, and user interfacing. The designed eNose was tested with experiment for detection of volatile components in water pollution, as a dimethyl disulphide or dimethyl diselenide or sulphur. Therefore, the results provide evidence that odor information can be recognized with around 86% efficiency, detecting smells unwanted in the water and improving the quality control in bottled water factories.

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“…The main drawback with such technology is that they are best integrated with large systems, which implies they will not be suitable to certain IoT applications, like in medical monitoring systems (Lian, 2015). Another note relates to the unstable nature of environmental power sources where power might be lost and devices are turned off, some researchers tackled this problem, like (Jayakumar et al, 2017), but data was lost even when power was restored.…”

Section: Related Workmentioning

confidence: 99%

A Power Saving Hybrid Technique for IoT

Al-Hamouz

2018

MAS

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Most IoT applications usually contain a battery inside, which provides good mobility for the system, but also limits lifetime of the application in general. A lot of work concentrated on power saving algorithms for software, and hardware solutions to provide better results. The proposed solution uses a hybrid technique that enables the IoT system to save power by installing a secondary low current microcontroller to put sensors and other devices in sleep mode, and depending on the IoT device's application, it will be awakened by a needed trigger that is tuned according to the application (such as certain sensors readings, or time based trigger). This will provide high system flexibility.

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Challenges in the design of self-powered wearable wireless sensors for healthcare Internet-of-Things

Cited by 12 publications

References 11 publications

Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices

Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices

Development of the MOOSY4 eNose IoT for Sulphur-Based VOC Water Pollution Detection

A Power Saving Hybrid Technique for IoT

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