Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

Liaqat, Sidrah; Dashtipour, Kia; Shah, Syed Aziz; Rizwan, Ali; Alotaibi, Abdullah Alhumaidi; Althobaiti, Turke; Arshad, Kamran; Assaleh, Khaled; Ramzan, Naeem

doi:10.1109/jsen.2021.3085362

Cited by 21 publications

(14 citation statements)

References 61 publications

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“…The trends of joint sensing can be separated into two aspects, the first one is joint training and second one is joint sensing [117]. Joint training will combine the WiFi signals and different types of data from other sources like skeleton key-points from images as ground truth datasets for the training step of the estimation system with WiFi signals.…”

Section: B Future Directions Of Wifi Sensing In Healthcarementioning

confidence: 99%

Contactless WiFi Sensing and Monitoring for Future Healthcare - Emerging Trends, Challenges, and Opportunities

Taha

Shah

et al. 2023

IEEE Rev. Biomed. Eng.

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WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users' bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the technology and discusses its use-cases from a perspective that considers hardware, advanced signal processing, and data acquisition.

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Section: B Future Directions Of Wifi Sensing In Healthcarementioning

confidence: 99%

Contactless WiFi Sensing and Monitoring for Future Healthcare - Emerging Trends, Challenges, and Opportunities

Taha

Shah

et al. 2023

IEEE Rev. Biomed. Eng.

Self Cite

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“…Random rotations were executed based on the individual user or device data dimensions. Once the random devices or users were selected for the FL process, model predictions were stacked using the traditional ensemble method of stacking [33].…”

Section: Personalized Patient Monitoringmentioning

confidence: 99%

FedStack: Personalized activity monitoring using stacked federated learning

Shaik¹,

Tao²,

Higgins³

et al. 2022

Knowledge-Based Systems

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“…Hence the power of the automated feature extraction approach, i.e. deep learning, has attracted increasing interest from various research areas such as cognitive assistance and context-aware systems [16], [17] and human activity recognition [18]- [20]. Different techniques based on deep learning have been proposed in activity recognition on various benchmark datasets.…”

Section: Related Workmentioning

confidence: 99%

Cross-Domain Activity Recognition Using Shared Representation in Sensor Data

et al. 2022

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Existing models based on sensor data for human activity recognition are reporting state-of-the-art performances. Most of these models are conducted based on single-domain learning in which for each domain a model is required to be trained. However, the generation of adequate labelled data and a learning model for each domain separately is often time-consuming and computationally expensive. Moreover, the deployment of multiple domainwise models is not scalable as it obscures domain distinctions, introduces extra computational costs, and limits the usefulness of training data. To mitigate this, we propose a multi-domain learning network to transfer knowledge across different but related domains and alleviate isolated learning paradigms using a shared representation. The proposed network consists of two identical causal convolutional sub-networks that are projected to a shared representation followed by a linear attention mechanism. The proposed network can be trained using the full training dataset of the source domain and a dataset of restricted size of the target training domain to reduce the need of large labelled training datasets. The network processes the source and target domains jointly to learn powerful and mutually complementary features to boost the performance in both domains. The proposed multi-domain learning network on six real-world sensor activity datasets outperforms the existing methods by applying only 50% of the labelled data. This confirms the efficacy of the proposed approach as a generic model to learn human activities from different but related domains in a joint effort, to reduce the number of required models and thus improve system efficiency.

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Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

Cited by 21 publications

References 61 publications

Contactless WiFi Sensing and Monitoring for Future Healthcare - Emerging Trends, Challenges, and Opportunities

Contactless WiFi Sensing and Monitoring for Future Healthcare - Emerging Trends, Challenges, and Opportunities

FedStack: Personalized activity monitoring using stacked federated learning

Cross-Domain Activity Recognition Using Shared Representation in Sensor Data

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