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Application of data fusion techniques and technologies for wearable health monitoring
Open accessAbstract
Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.
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“One of the most significant advantages of AI-based wearable health technology is to promote preventive healthcare.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [15]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147
An introduction to multisensor data fusion
Hall, D. L., & Llinas, J. (1997) · Proceedings of the IEEE · doi:10.1109/5.554205 · Cited by 2,629
A review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396
Detecting Stress During Real-World Driving Tasks Using Physiological Sensors
Healey, J. A., & Picard, R. W. (2005) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2005.848368 · Cited by 2,207 · cited 1×
“A system presented by Healey and Picard [96] was able to classify stress during real-world driving tasks into three levels based on wearable sensors including two skin conductivity sensors, ECG, EMG, chest expansion respiration sensor.”
Flagged first, then by citations
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Items 101–182 of 182
Remote Optical Sensing of Heart Sounds for Biometric Identification Using Information Fusion
Li, H., Vaškeviciute, M., Starshynov, I., & Faccio, D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3457967 · Cited by 6
“Information fusion technology [24], [25], [26] has been widely used to leverage the strength of different sensing methods in activity and gesture recognition [25], [27], [28], navigation and localization [29], [30], as well as biometric identification [31], [32], [33], [34], [35], [36].”
BackgroundMethodTajitsu, Y., Shimda, S., Nonomura, T., Yanagimoto, H., Nakamura, S., Ueshima, R., Kawanobe, M., Nakiri, T., Takarada, J., Takeuchi, O., et al. (2023) · Micromachines · doi:10.3390/mi15010086 · Cited by 6
Li, P., Singh, J., Cui, H. F., & Boano, C. A. (2024) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2024.101944 · Cited by 6
Distributed Zonotopic Fusion Estimation for Multisensor Systems
Zhang, Y., Chen, B., Wang, Z., Zhang, W., Yu, L. S., & Guo, L. (2025) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2025.3647564 · Cited by 5
“A. Background and Motivation I N RECENT decades, multisensor fusion estimation has attracted considerable research interest due to its broad applications, including target tracking [1], [2], [3], localization [4], [5], environmental monitoring [6], [7], and health monitoring [8], [9].”
Fink, O., Nejjar, I., Sharma, V., Niresi, K. F., Sun, H., Dong, H., Xu, C., Wei, A., Bizzi, A. A. C., Theiler, R., et al. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112376 · Cited by 21
Exploration of AI-Enhanced Wearable Devices for Advanced Cardiovascular Monitoring in the Elderly
Cafolla, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-67278-1_15 · Cited by 5
Çelik, Y., Stuart, S., Woo, W. L., Pearson, L. T., & Godfrey, A. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9870909 · Cited by 5
“Alternatively, k-NN has a simple structure measuring distance between unlabelled observations and training samples to perform classification [22].”
BackgroundResultDjemili, R., & Zamouche, M. (2023) · International Journal of Computers and Applications · doi:10.1080/1206212x.2023.2198785 · Cited by 5
“Human Activity Recognition (HAR) gained prominence in the frontiers of research due to its diverse range of real-world applications ranging from smart home design, sports activity tracking, motion capture on game consoles, and health monitoring [4–6].”
BackgroundAkan Veri Kümeleme Teknikleri Üzerine Bir Derleme
Şenol, A., & Karacan, H. (2018) · European Journal of Science and Technology · doi:10.31590/ejosat.446019 · Cited by 5
“Kullanıcıya ait click verisi analizi [6], saldırı tespit sistemleri [7-9], sosyal medya [10-12], finansal uygulamalar [13], bilimsel araştırmalar [14], sağlık araştırmaları [15-17], mobil uygulamalar [18], nesnelerin interneti (IoT) [19] ve sensor ağ [20, 21] gibi pek çok alanda kullanılmaktadır.”
Big Data in Gesundheitswesen und Medizin
Stefan Rüping, A., & Sander, J. (2018) · Springer eBooks · doi:10.1007/978-3-662-57611-3_2 · Cited by 6
Surf Session Events’ Profiling Using Smartphones’ Embedded Sensors
Gomes, D., Moreira, D., da Costa, J. C., Graça, R., & Madureira, J. L. (2019) · Sensors · doi:10.3390/s19143138 · Cited by 5
“…overcome the most common inertial measurement-related errors (e.g., drift), usually based on sensor fusion methods which try to compensate for each sensor’s well-known sources of error by employing sophisticated filtering techniques, which ultimately output reliable orientation estimations [16,20].”
BackgroundResilient Software Architecture Platform for the Individual Care Plan
Mendes, D. J. M., Lopes, M. J., García-Alonso, J. M., Santos, J. M. A., & Sousa, L. (2020) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-1937-0.ch002 · Cited by 5
An Autonomous Marine Mucilage Monitoring System
Sanver, U., & Yeşildirek, A. (2023) · Sustainability · doi:10.3390/su15043340 · Cited by 5
Engineering Human Gait and the Potential Role of Wearable Sensors to Monitor Falls
Sejdić, E., Godfrey, A., McIlroy, W. E., & Montero‐Odasso, M. (2019) · Springer eBooks · doi:10.1007/978-3-030-24233-6_22 · Cited by 5
Toptsis, M., Karkanis, N., Giannakoulas, A., & Kaifas, T. N. F. (2026) · Electronics · doi:10.3390/electronics15020295 · Cited by 10
Intelligent Fusion: A Resilient Anomaly Detection Framework for IoMT Health Devices
Pastore, F., Anwar, R. W., Jabeur, N. H., & Ali, S. (2026) · Information · doi:10.3390/info17020117 · Cited by 6
“…of work has begun to explore data fusion approaches for addressing this issue [8,9], the development of a unified framework capable of a holistic evaluation of the IoMT ecosystem by contextually differentiating between these distinct anomaly classes continues to be a main research challenge [10].”
Medical Image Fusion Using Discrete Wavelet Transform: In view of Deep Learning
Thakur, N. V., Yenurkar, G. K., Aherrao, A., Aherrao, A., Landge, S., & Katre, S. (2023) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaiei58380.2023.10406337 · Cited by 4
“King et al. [2] discussed about various data fusion techniques to process the data collected from different wearable sensors.”
BackgroundData management and visualization of wearable medical devices assisted by artificial intelligence
Zhao, M., Wang, D., & Li, J. (2021) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-021-00328-0 · Cited by 4
[Retracted] Software Vulnerability Mining Techniques Based on Data Fusion and Reverse Engineering
Liu, T. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/4329034 · Cited by 4
RetractedWearable Structured Mental-Sensing-Graph Measurement
Yang, D., Gao, B., Woo, W. L., Wen, H., Zhao, Y., & Gao, Z. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3232163 · Cited by 5
BmmW: A DNN-Based Joint BLE and mmWave Radar System for Accurate 3D Localization
Li, P., Singh, J., Cui, H., & Boano, C. (2023) · 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot58021.2023.00016 · Cited by 9
“1 A comprehensive overview of data fusion algorithms is presented in [35].”
BackgroundMultimode Data Fusion Based Remote Healthcare Framework
Wang, L., Sun, W., -, Y. C., Li, P., & Zhao, L. (2018) · International Conference Big Data Engineering and Technology · doi:10.1145/3297730.3297733 · Cited by 4
“The data acquisition layer mainly completes two parts: real-time collection of human health data and networking and data transmission of body area network wireless communication network[12].”
BackgroundAlvarez-Ambrosio, M., Chausa, P., Moreno-Blanco, D., Roca‐Ventura, A., García, I. O., Cattaneo, G., González, P. S., Sánchez, J. S., & Gómez, E. J. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1710891 · Cited by 3
Multi-sensor data fusion perspective for smart grid analytics
Daş, R., & Tuna, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14066-2.00006-2 · Cited by 3
Early-Stage Sensor Data Fusion Pipeline Exploration Framework for Agriculture and Animal Welfare
Martin, D., Roberts, D. L., & Bozkurt, A. Y. (2025) · AgriEngineering · doi:10.3390/agriengineering7070215 · Cited by 3
A Linux-Based Integrated Structural Health Monitoring System for Bridges in Remote Regions
Yan, Y., Wu, Z., Wu, X., Zhou, X., & Weng, C. (2019) · Instrumentation Mesure Métrologie · doi:10.18280/i2m.180603 · Cited by 3
“[19] reviewed the data fusion techniques and Instrumentation Mesure Métrologie”
MethodMochizuki, L., & Araújo, B. A. (2021) · Modapalavra e-periódico · doi:10.5965/1982615x14322021030 · Cited by 3
Improving Daily Routine Recognition in Hearing Aids Using Sequence Learning
Kuebert, T., Puder, H., & Koeppl, H. (2021) · IEEE Access · doi:10.1109/access.2021.3092763 · Cited by 3
“Here, the situational intention needs to be better decoded from suitable motion patterns or further sensors [36], e.g., electromyograms for listening attention [37], that can deliver a more reliable input.”
BackgroundDetection and characterization of surfing events with smartphones’ embedded sensors
Gomes, D., Moreira, D., & Madureira, J. L. (2018) · International Conference on Sensing Technology · doi:10.1109/icsenst.2018.8603656 · Cited by 3
“low processing power and are available on multiple devices (including the majority of the smartphones) [16]–[18].”
BackgroundGoodison, S. E., Barnum, J. D., Vermeer, M., Woods, D., Sitar, S. I., Shelton, S. R., & Jackson, B. A. (2020) · RAND Corporation eBooks · doi:10.7249/rra108-7 · Cited by 3
A review of data fusion technology for fault diagnoses in wind turbine components
Owolabi, O. I., Adedeji, P. A., Olatunji, O. O., & Madushele, N. (2026) · Next Energy · doi:10.1016/j.nxener.2026.100609 · Cited by 2
Osonuga, A., Dave, M., Ogieuhi, I. J., Olawade, D. B., & Boussios, S. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16070377 · Cited by 3
“Recent developments in consumer wearables include enhanced sensor fusion capabilities, combining multiple data streams to improve accuracy and reduce false positives [11].”
Automatic analysis of the heart sound signal to build smart healthcare system
Jain, P. K., & Mahela, O. P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-385845-8.00012-5 · Cited by 2
Weakening the Bullwhip Effect in the Supply Chain Based on Data Fusion
Wang, Z., & Cheng, G. (2022) · International Journal of Distributed Systems and Technologies · doi:10.4018/ijdst.307945 · Cited by 2
Introduction to Healthcare-Oriented Monitoring of Persons
Wagner, J., Mazurek, P., & Morawski, R. Z. (2022) · Health information science · doi:10.1007/978-3-030-96009-4_1 · Cited by 2
Salunke, G., Singh, V. K., & Jadhav, C. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.s02.24 · Cited by 2
“A snapshot of existing commercially available sensors, concentrating on their sensing capacity, is also presented, along with a remark on the gaps that must be overcome to bring research to market[26].”
BackgroundShah, V. V., Jadav, S., Goyal, S., & Palanthandalam‐Madapusi, H. J. (2023) · Applied Sciences · doi:10.3390/app13179502 · Cited by 3
“The proposed methodology has potential applications in other situations, including motor performance in multiple sclerosis [70], stability of balance control [71,72], characterizing slow eye movements [73], wearable health monitoring [74], and cerebral autoregulation [75,76].”
MethodTung, J. Y., Gower, S., Ooteghem, K. V., Nouredanesh, M., & Gage, W. H. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818914-6.00008-9 · Cited by 2
Khoshbakht, F., Kaur, J., Verawaty, Hutagaol, J., Anantadjaya, S. P. D., & Quadri, S. M. K. (2022) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca54612.2022.9985469 · Cited by 1
“It analyse new information in context of historical data, which can make informed decisions regarding marketing that have failed in past [18, 21].”
BackgroundRetractedMensah, D. K., Ofori, M. A., Orwa, G. O., & Hewson, P. (2025) · Journal of Multidisciplinary Applied Natural Science · doi:10.47352/jmans.2774-3047.275 · Cited by 1
Wearable sensing technologies in construction safety and health: An umbrella review
Huang, Y., Wu, K., Wang, B., Hu, Q., Duan, S., Chu, H., Wang, D., & Sun, B. (2026) · Lifeline Emergency and Safety · doi:10.26599/lles.2025.9660016 · Cited by 1
Mao, J., Chen, L., Xia, W., Gong, J., Liang, C., Zhang, Y., & Wu, D. (2026) · Measurement · doi:10.1016/j.measurement.2026.121152 · Cited by 1
Smart Sensor Technologies and Wearable IoT for Healthcare
Sun, H., Shukla, R. P., & Daniele, M. A.-A. (2026) · Springer eBooks · doi:10.1007/978-3-032-06286-4_4 · Cited by 1
Measurements of Wearable Noninvasive Transducers for Sport Performance Improvement
Gueli, A. M., Ferrante, M., Trigona, C., Failla, A., & Fiore, M. (2019) · IEEE International Symposium on Measurements & Networking (M&N) · doi:10.1109/iwmn.2019.8805026 · Cited by 1
Estudo Prospectivo sobre Biossensores de Aplicação Militar
Marson, R. A., & Guimarães, R. W. A. (2021) · Revista Agulhas Negras · doi:10.70545/ran.v5i5.6928 · Cited by 1
A Framework for IoT Data Collection and Fusion in Infectious Diseases Surveillance
Jillahi, K. B., & Thandekkattu, S. G. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-5498-8.ch009 · Cited by 1
Vision and Sensor Fusion for Non-Invasive Health Monitoring
Gomathi, N., Reddy, V., & J.Mahender (2025) · International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · doi:10.1109/icicv64824.2025.11085524 · Cited by 1
“The system is capable of automatically detecting the presence of humans through vision-based methods and triggering sensor readings only when required [2].”
Wearable Capacitive Patches for Data Fusion in Biomedical Monitoring & Physical Activity
Rohit, A., Kelestemur, Y., Runyon, J. C., & Kaya, S. (2019) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas.2019.8884812 · Cited by 1
Muhammad, N., & Lim, T. H. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-69963-5_11 · Cited by 1
Sensor Integration for Gait Analysis
Çelik, Y., Vitório, R. R., Powell, D., Moore, J., Young, F., Coulby, G., Tung, J. Y., Nouredanesh, M., Ellis, R., Izmailova, E. S., et al. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822548-6.00139-4 · Cited by 1
Mydhili, S. K., K, D. R. M. S., Fathima Naseera, M., Ranjith Kumar, R., & Rohith, N. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4157666 · Cited by 1
Shah, V. V., Jadav, S., Goyal, S., & Palanthandalam‐Madapusi, H. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.17.512460 · Cited by 1
Leliveld, L. M. C., Brandolese, C., Grotto, M., Marinucci, A., Fossati, N., Lovarelli, D., Riva, E., & Provolo, G. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4511074 · Cited by 1
Human motion data expansion from arbitrary sparse sensors with shallow recurrent decodersPreprint
Ebers, M. R., Pitts, M. N., Kutz, J. N., & Steele, K. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.01.596487 · Cited by 1
Homescu, A. T. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3187926
A data fusion-based hybrid sensory system for older people’s daily activity recognition.
Wang, Y. (2019) · Bournemouth University Research Online (Bournemouth University)
Analysis of Health Applications on Smart phones and Wearable Devices
Espineli, J. D., & Niguidula, J. (2018) · doi:10.1145/3265007.3265012
“This is made possible because of the wearable sensor used to capture human motions which includes inertial sensors such as accelerometers, gyroscopes, and magnetometers [4].”
Background40th Anniversary Issue: Reflections on papers from the archive on “Biomechanics”
Black, R. A., & Houston, G. (2019) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2019.09.007
Şenol, A., & Karacan, H. (2019) · European Journal of Science and Technology · doi:10.31590/ejosat.638096
IOT-based fluid and heartbeat monitoring for advanced healthcare
Banerjee, I., & Madhumathy, P. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-818004-4.00008-x
Development of Capacitive Wearable Patches and Bands for Data Fusion in Complex Physical Activities
Rohit, A., Kelestemur, Y., Runyon, J. C., & Kaya, S. (2019) · IEEE International Flexible Electronics Technology Conference (IFETC) · doi:10.1109/ifetc46817.2019.9073726
A Comprehensive Healthcare Model for a Smart Left Ventricle Assisting Device
Ernest, P. J., Chavan, A., & Rao, A. S. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-3338-9_7
A Preliminary Study for Automatic Activity Labelling on an Elder People ADL Dataset
de la Cal, E., Fáñez, M., DaSilva, A., Villar, J. R., Sedano, J., & Suárez, V. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-57802-2_2
Ultra-Flexible and Durable Textile Capacitors with Piezoelectric PVDF Dielectrics for Wearables
Rohit, A., Kelestemur, Y., & Kaya, S. (2020) · IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) · doi:10.1109/fleps49123.2020.9239561
“Actual data fusion algorithms, which is still under development and outside the scope of this paper, is largely based on probabilistic (Kalman filtering and Bayesian) approaches that must be finely tuned according to a given application [12].”
MethodChatterjee, S., Dey, D., & Munshi, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91211-2.00004-4
Wang, Z., Li, J., Jin, Y., Wang, J., Yang, F. C., Li, G., Ni, X., & Ding, W. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103571
Varshney, P. K. (2021) · Springer eBooks · doi:10.1007/978-3-030-63416-2_301
Varshney, P. K. (2020) · Springer eBooks · doi:10.1007/978-3-030-03243-2_301-1
Bolić, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-820947-9.00010-6
Wearable Health Monitoring System on Stationary Bicycle
Riyadi, M. A., Sandiono, L., Setiyono, B., & Sumardi, S. (2023) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee58992.2023.10405003
Data Fusion in Wireless Sensor Network: An Overview
Bernal, D., Héctor Kaschel, C., & Kern, J. (2023) · IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) · doi:10.1109/chilecon60335.2023.10418610
“Third type of DF techniques classification [9].”
BackgroundAssessing Safety of an Automated Vehicle Through Model-Driven Analysis and Simulation
Adedjouma, M., Gaudin, F., & Fiani, P. (2023) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec60848.2023.00063
“…the objects detected in the scene including their characteristics: envelope, speed, position of the obstacle The Perception relies upon LIDAR and Camera sensors, and an ML/DL data fusion component [4] which aims to increase accuracy by integrating different, sometimes redundant data sources.”
BackgroundSingh, C. K. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1444-0.ch007
Synchronization of Wearable Sensor Data for Vital Sign Monitoring
Lai, J. C. Y., & Kar, P. (2025) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe66822.2025.00025
“Accurate timestamps are crucial for the fusion of information from different modalities or sensors to enable medical interpretability [1].”
Smartphone-Based Commercialized Biomedical Devices
Shukla, V. K., Kumari, P., & Sengar, M. S. (2026) · Springer eBooks · doi:10.1007/978-981-95-5519-2_8
Priya, V. V., Saranya, P., Usharani, R., Hemavathi, D., Viji, D., & Balasubramaniyan, D. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-38349-6.00003-8
Jeong, K., Livanelioglu, C., Liao, J., Lee, I., & Jang, T. (2026) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2026.3685168 · Cited by 1
“[5], [6] In addition, flexible electrode patches and miniaturized bio-signals’ acquisition devices have been demonstrated to monitor bio-impedance and bio-signals [7], [8].”
Periferakis, A., Troumpata, L., Periferakis, A.-T., Periferakis, K., Timofticiuc, I.-A., Georgatos-Garcia, S., Touriki, G., Bozatzi, A., Săvulescu-Fiedler, I., & Iftime, A. (2026) · Balneo and PRM Research Journal · doi:10.12680/balneo.2026.1019
Predicting behaviours of beef calves from accelerometer data: A machine learning framework
Coon, R. E., Camargo, V. A., & Pajor, E. A. (2026) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2026.107143
A comprehensive survey of smart medical wearables in fitness and health monitoring
Kiruthika, M., Kanagaraj, K., & Samuel, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27761-0.00007-x
Lonkar, L. K., & Malarkodi, B. (2026) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-026-01249-2
Metadata
- Authors
- King, R. C., Villeneuve, E., White, R. J., Sherratt, R. S., Holderbaum, W., & Harwin, W. S.
- Affiliations
- University of Reading (GB)
- University of Exeter (GB)
- Corresponding author
- King, Rachel C.
- Venue
- Medical Engineering & Physics
- Publisher
- IOP Publishing
- Published
- 2017-02-23
- Volume
- 42
- Issue
- 1
- Pages
- 1-12
- ISSN
- 1350-4533 (ISSN-L)
- 1873-4030
- OpenAlex
- W2588424569
- Cited by
- 182
- References
- 124
- Citations by year
- 2026: 18 · 2025: 10 · 2024: 15 · 2023: 22 · 2022: 25 · 2021: 24 · 2020: 20 · 2019: 24 · 2018: 20 · 2017: 4
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Engineering & Physics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- e-space (Manchester Metropolitan University) (acceptedVersion)
- CentAUR (University of Reading) (submittedVersion)
- Aston Publications Explorer (Aston University) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/K031910/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Keywords
- data fusion, wearable sensors, health monitoring, physiological monitoring, activity monitoring, ambulatory monitoring, gait analysis, fall detection, biometric monitoring, ubiquitous health monitoring
- MeSH terms
- Wearable Electronic Devices, Algorithms, Humans, Monitoring, Physiologic, Statistics as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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