Study of a Smart Cup for Home Monitoring of the Arm and Hand of Stroke Patients

Bobin, Maxence; Boukallel, Mehdi; Anastassova, Margarita; Ammi, Mehdi

doi:10.1145/2982142.2982188

Cited by 2 publications

(2 citation statements)

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“…Similarly, temporal and spatial-temporal methods are also developed to detect the abnormal behavior from the environmental datasets [49]. The visualization technique is also developed over the clustering methods to identify and predict the anomalous behavior of the under-observation system [48], [50]. In Table 8, we present our findings based on machine learning methods in the area of smart objects.…”

Section: ) Machine Learning Methodsmentioning

confidence: 99%

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

2019

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Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact in diverse application domains. Sensors generate a huge amount of data while monitoring the physical spaces and objects. These huge collected data streams can be analyzed to identify unhealthy behaviors. It may reduce functional risks, avoid unseen problems, and prevent downtime of the systems. Many research methodologies have been designed and developed to determine such anomalous behaviors in security and risk analysis domains. In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. We focus on the studies published from 2000 to 2018 in the application areas of intelligent inhabitant environments, transportation systems, health care systems, smart objects, and industrial systems. We have identified a number of research gaps related to the data collection, the analysis of imbalanced large datasets, limitations of statistical methods to process the huge sensory data, and few research articles in abnormal behavior prediction in real scenarios. Based on our analysis, researchers and practitioners can acquaint themselves with the existing approaches, use them to solve real problems, and/or further contribute to developing novel techniques for anomaly detection, prediction, and analysis.INDEX TERMS Statistical learning, machine learning, intelligent environments, smart objects, intelligent transportation systems, industrial systems.

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Section: ) Machine Learning Methodsmentioning

confidence: 99%

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

2019

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“…The evaluation on 13 subjects demonstrates the usefulness of the system in recognizing the muscle strength of stroke patients without wearing any devices. Meanwhile, Bobin et al [22] introduced a system to monitor and guide stroke patients. The system consists of a smart mug that tracks the patient's drinking activities.…”

Section: Rehabilitation Monitoringmentioning

confidence: 99%

Pervasive Healthcare Internet of Things: A Survey

et al. 2022

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Thanks to the proliferation of the Internet of Things (IoT), pervasive healthcare is gaining popularity day by day as it offers health support to patients irrespective of their location. In emergency medical situations, medical aid can be sent quickly. Though not yet standardized, this research direction, healthcare Internet of Things (H-IoT), attracts the attention of the research community, both academia and industry. In this article, we conduct a comprehensive survey of pervasive computing H-IoT. We would like to visit the wide range of applications. We provide a broad vision of key components, their roles, and connections in the big picture. We classify the vast amount of publications into different categories such as sensors, communication, artificial intelligence, infrastructure, and security. Intensively covering 118 research works, we survey (1) applications, (2) key components, their roles and connections, and (3) the challenges. Our survey also discusses the potential solutions to overcome the challenges in this research field.

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Study of a Smart Cup for Home Monitoring of the Arm and Hand of Stroke Patients

Cited by 2 publications

References 3 publications

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Pervasive Healthcare Internet of Things: A Survey

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