Activity inference engine for real-time cognitive assistance in smart environments

Jiang, Hao; Bouzouane, Abdenour; Bouchard, Bruno; Gaboury, Sébastien

doi:10.1007/s12652-017-0467-7

Cited by 17 publications

(8 citation statements)

References 51 publications

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“…MTC types 3 and 5 could not be evaluated in this experiment due to lack of instances in the prescription dataset. [9,20,22] and thus rely on differing inputs, comparing the performance of HERBERT with these models is not fair. Hence, we consider only statistical models as our baselines for time series prediction.…”

Section: Effect Of Varying Windowmentioning

confidence: 99%

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ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence

Seegmiller¹,

Gatto²,

Mamun³

et al. 2023

Preprint

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Prescription medications often impose temporal constraints on regular health behaviors (RHBs) of patients, e.g., eating before taking medication. Violations of such medical temporal constraints (MTCs) can result in adverse effects. Detecting and predicting such violations before they occur can help alert the patient. We formulate the problem of modeling MTCs and develop a proof-of-concept solution, ActSafe, to predict violations of MTCs well ahead of time. ActSafe utilizes a context-free grammar based approach for extracting and mapping MTCs from patient education materials. It also addresses the challenges of accurately predicting RHBs central to MTCs (e.g., medication intake). Our novel behavior prediction model, HERBERT , utilizes a basis vectorization of time series that is generalizable across temporal scale and duration of behaviors, explicitly capturing the dependency between temporally collocated behaviors. Based on evaluation using a real-world RHB dataset collected from 28 patients in uncontrolled environments, HERBERT outperforms baseline models with an average of 51% reduction in root mean square error. Based on an evaluation involving patients with chronic conditions, ActSafe can predict MTC violations a day ahead of time with an average F1 score of 0.86.

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Section: Effect Of Varying Windowmentioning

confidence: 99%

“…Predictive modeling of human behavior is a burgeoning field, with an increased focus on sensor-based activity recognition models [20,22,32]. There are three main formulations of the sensor-based activity prediction task.…”

Section: Predictive Modeling Of Regular Health Behaviors (Rhbs)mentioning

confidence: 99%

ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence

Seegmiller¹,

Gatto²,

Mamun³

et al. 2023

Preprint

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“…The identified solutions and services are then delivered to the occupants through either prompts or interventions. While the majority of the articles targeted a cognitively impaired demographic group in general [40,57,58,[62][63][64][65][66], others carried out their studies specifically on a sub-category of cognitive impairment such as traumatic brain injury (TBI) [67,68], dementia [56,59,61,[69][70][71], stroke rehabilitation [56], Alzheimer Disease [6,9], Parkinson Disease [72], Apraxia, and action disorganization syndrome [73].…”

Section: Occupantsmentioning

confidence: 99%

“…The systems can then detect that the occupants perform an activity erroneously as a result of deviating from the learnt pattern [65,66]. To create activity models, Moutacalli et al [66] collected ordered activated sensors with duration time between adjacent sensors.…”

Section: Activity Recognitionmentioning

confidence: 99%

Intelligence Complements from the Built Environment: A Review of Cps-Enabled Smart Buildings for Cognitively Declined Occupants

Alimoradi

Gao

2022

SSRN Journal

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“…In real-time systems, activity recognition can either happen in a closed or open universe. In a closed universe, a complete set of activities is defined from the start [ 54 ], and any new collected data forming a feature vector will be classified in one of the classes of this set, or remain an unclassified instance in some cases [ 89 ]. In an open universe, new activities can be discovered as the system runs, and irrelevant activities can be discarded.…”

Section: Real-time Centralized Activity Recognitionmentioning

confidence: 99%

From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review

Baghezza

Bouchard

Bouzouane

et al. 2021

Sensors

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This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare. Offline activity recognition is discussed as a starting point to establish the useful concepts of the field, such as sensor types, activity labeling and feature extraction, outlier detection, and machine learning. New challenges and obstacles brought on by real-time centralized activity recognition such as communication, real-time activity labeling, cloud and local approaches, and real-time machine learning in a streaming context are then discussed. Finally, real-time distributed activity recognition is covered through existing implementations in the scientific literature, and six main angles of optimization are defined: Processing, memory, communication, energy, time, and accuracy. This survey is addressed to any reader interested in the development of distributed artificial intelligence as well activity recognition, regardless of their level of expertise.

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Activity inference engine for real-time cognitive assistance in smart environments

Cited by 17 publications

References 51 publications

ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence

ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence

Intelligence Complements from the Built Environment: A Review of Cps-Enabled Smart Buildings for Cognitively Declined Occupants

From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review

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