CompRate: Power Efficient Heart Rate and Heart Rate Variability Monitoring on Smart Wearables

Dissanayake, Vipula; Elvitigala, Don Samitha; Zhang, Haimo; Weerasinghe, Chamod; Nanayakkara, Suranga

doi:10.1145/3359996.3364239

Cited by 6 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This observation can be explained based on the findings reported in the literature: when someone experiences an emotion, bodily reaction reflects faster with heartbeat compared to body temperature variations and skin conductance changes [42]. Also, literature [43,44] suggest a higher correlation between heart pulse and wrist accelerometer readings, providing better accuracy for the accelerometer. Interestingly, when there is no data loss, the prediction accuracy of the combined model is not significantly (p > 0.05) higher than the prediction accuracy of any individual signal.…”

Section: ) Resultsmentioning

confidence: 86%

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

Extracting emotions from physiological signals has become popular over the past decade. Recent advancements in wearable smart devices have enabled capturing physiological signals continuously and unobtrusively. However, signal readings from different smart wearables are lossy due to user activities, making it difficult to develop robust models for emotion recognition. Also, the limited availability of data labels is an inherent challenge for developing machine learning techniques for emotion classification. This paper presents a novel self-supervised approach inspired by contrastive learning to address the above challenges. In particular, our proposed approach develops a method to learn representations of individual physiological signals, which can be used for downstream classification tasks. Our evaluation with four publicly available datasets shows that the proposed method surpasses the emotion recognition performance of state-of-the-art techniques for emotion classification. In addition, we show that our method is more robust to losses in the input signal.

show abstract

Section: ) Resultsmentioning

confidence: 86%

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, H. Wu et al have created a tool called SENTINEL capable of generating automatic tests detecting sensor leaks in Android and Android Wear [40]; while R. Lutze et al present an innovative approach to the creation of apps for smartwatches based on the need for dialogue management, a distributed and layered architecture and keeping maintenance costs to a minimum [25]. Finally, R. Rawassizadeh et al describe an energy-efficient lightweight framework that integrates continuous context sensing with a prediction for wearables [33]; while V. Dissanayake et al have created a low-power solution to estimate heart rate over an extended period of time based on data produced by the accelerometer, and demonstrating that these results are comparable to those of the dedicated PPG sensor, but with better battery life [12].…”

Section: Related Workmentioning

confidence: 99%

Real-time Energy-efficient Sensor Libraries for Wearable Devices

Calisti,

Lattanzi

2023

Preprint

View full text Add to dashboard Cite

The growing popularity of wearable technology has led to a surge in smartwatch usage among the general public. These devices offer a range of features, including internet connectivity, fitness tracking, and real-time notifications, making them valuable tools for staying connected to the online world while remaining engaged in real-world activities. Smartwatches have become powerful platforms for Human Activity Recognition (HAR) applications thanks to the increasing computational power and the presence of a wide array of sensors, such as accelerometers, gyroscopes, heart rate, and step counters. Efficient real-time data collection from internal sensors is a crucial requirement for HAR applications on wearable devices due to their constraints in battery size and duration. In this paper, we introduce the implementation of three energy-efficient user-level libraries developed for real-time data collection from inertial sensors using native Wear OS APIs and different techniques: Thread, Flow, and Channel. Experiments were conducted on a commercially available Oppo smartwatch comparing them in terms of code size, memory utilization, and energy consumption. The characterization results demonstrate empirically that the solution is lightweight and adaptable, making it suitable for development on wearable devices without significant impact on battery life and system performance.

show abstract

“…The ageing of the population makes people pursue better health, and at the same time, the demand for real-time health monitoring and early warning is increasing, which has motivated the emergence of wearable health monitoring and testing equipment, for example, wristbands, blood four index monitors, blood oxygen meters [1], etc. Nursing homes use these wearable health monitoring and testing equipment to monitor and detect individuals' daily exercise, sleep quality, and physiological indicators in real time, such as steps, blood oxygen, blood pressure, heart rate [2], blood sugar, sweat, tears, etc.…”

Section: Introductionmentioning

confidence: 99%

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Zhou

et al. 2023

Electronics

View full text Add to dashboard Cite

With the innovation of technologies such as sensors and artificial intelligence, some nursing homes use wearable devices to monitor the movement and physiological indicators of the elderly and provide prompts for any health risks. Nevertheless, this kind of risk warning is a decision based on a particular physiological indicator. Therefore, such decisions cannot effectively predict health risks. To achieve this goal, we propose a model Lidom (A LightGBM-based Disease Prediction Model) based on the combination of the LightGBM algorithm, InterpretML framework, and sequence confrontation network (SeqGAN). The Lidom model first solves the problem of uneven samples based on the sequence confrontation network (SeqGAN), then trains the model based on the LightGBM algorithm, uses the InterpretML framework for analysis, and finally obtains the best model. This paper uses the public dataset MIMIC-III, subject data, and the early diabetes risk prediction dataset in UCI as sample data. The experimental results show that the Lidom model has an accuracy rate of 93.46% for disease risk prediction and an accuracy rate of 99.8% for early diabetes risk prediction. The results show that the Lidom model can provide adequate support for the prediction of the health risks of the elderly.

show abstract

CompRate: Power Efficient Heart Rate and Heart Rate Variability Monitoring on Smart Wearables

Cited by 6 publications

References 18 publications

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

SigRep: Toward Robust Wearable Emotion Recognition With Contrastive Representation Learning

Real-time Energy-efficient Sensor Libraries for Wearable Devices

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Contact Info

Product

Resources

About