CAL: A Smart Home Environment for Monitoring Cognitive Decline

Fredericks, Erik M.; Bowers, Kate M.; Price, Katey A.; Hariri, Reihaneh H.

doi:10.1109/icdcs.2018.00155

Cited by 10 publications

(8 citation statements)

References 24 publications

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“…In terms of aims and objectives, research suggests that embedded devices installed in the home to monitor patient health and safety may delay institutionalization [ 123 ], and therefore more emphasis should be placed on the feasibility of remote evaluations. To this end, we propose that future research focuses on natural conversations, which are straightforward to collect passively, continuously, and longitudinally in order to monitor cognitive health through an AI system.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Garcia

Ritchie

Luz

2020

JAD

136

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Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Garcia

Ritchie

Luz

2020

JAD

136

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“…Thus, it is important for them to be able to visualize collected data in a way that is intuitive and relevant for clinical decision making [31]. Other studies have used machine learning approaches to predict conditions such as cognitive decline [32][33][34][35][36]. For example, Dawadi et al [33] used statistical features (variances, autocorrelation, skewness, kurtosis, and change) of daily activity behavior (ie, total sensor events, cook duration, sleep duration) to train machine learning algorithms to predict the clinical assessment scores.…”

Section: Ambient Assisted Livingmentioning

confidence: 99%

Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report (Preprint)

Lussier¹,

Aboujaoudé²,

Couture³

et al. 2020

Preprint

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BACKGROUND Many older adults choose to live independently in their home for as long as possible, despite psychosocial and medical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges in allocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted living (AAL) to better support care recipients. To be effective, these technologies should be able to report on clinically relevant changes in order to support decision-making at an individual level. OBJECTIVE This project examined the concurrent validity of AAL monitoring reports and information gathered by care professionals using triangulation. METHODS This longitudinal single-case study spans over 490 days of monitoring of a 90-year-old woman with Alzheimer’s disease (AD) receiving support from local health-care services. A clinical nurse in charge of her health and social care was interviewed three times during the project. Linear mixed models for repeated measures were used to analyze each daily activity (i.e. sleep, outing activities, periods of low mobility, cooking related activities and hygiene-related activities). Significant changes observed in data from monitoring reports were compared with information gathered by the care professional to explore with concurrent validity. RESULTS Over time, the monitoring reports showed evolving trends in the care recipient’s daily activities. Significant activity changes occurred over time regarding sleep, outings, cooking, mobility and hygiene-related activities. While the nurse observed some trends, the monitoring reports highlighted information that she had not yet identified. Most trends detected in the monitoring reports are consistent with clinical information gathered by the nurse. Also, the AAL system detected changes in daily trends following an intervention specific to meal preparation. CONCLUSIONS Overall, trends identified by AAL monitoring were found to be consistent with clinical reports. They helped answer the nurse’s questions and helped her develop interventions to maintain the care recipient at home. These findings suggest the vast potential of AAL technologies to support health care services and aging in place by providing valid and clinically relevant information over time about activities of daily living. Such data is essential when other sources yield incomplete information for decision-making.

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“…Other approaches similar to 3CAP use NLP to detect or predict Alzheimer's disease in patients [6,20,23] but do not extend their evaluations to smart homes. Although certain smart homes address a variety of health care needs, including healthy, autistic, or elderly users [9,14], the progressive nature of Alzheimer's disease may be better addressed with an adaptive smart home [11].…”

Section: Related Workmentioning

confidence: 99%

“…of moving to a dementia unit at a nursing care facility (e.g., stress, costs, cognitive decline) can be delayed if the individual is allowed to stay at home for as long as possible [24]. Research suggests that installing a variety of embedded devices that monitor patient health and safety in the home can delay institutionalization [11]. Establishing a medically-oriented smart home for early-stage Alzheimer's patients may help combat the impending manpower deficit, provide a cost-effective solution for patients, and reduce the demand on family or friends to become full-time caregivers.…”

mentioning

confidence: 99%

“…CAL (Cognitive Assisted Living), an architecture for a self-adaptive smart home system, was recently introduced to support Alzheimer's patients by changing its configurations at run time in response to the decline in the patient's cognitive health [11]. Configuration changes may include overriding patient decisions in the household if the decisions put the patient in a potentially dangerous state (e.g., if the patient releases gas in a fireplace but forgets to ignite it).…”

mentioning

confidence: 99%

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3CAP: categorizing the cognitive capabilities of Alzheimer’s patients in a smart home environment

Bowers

Hariri

Price

2018

Proceedings of the 4th ACM SIGSOFT International Workshop on NLP for Software Engineering

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Alzheimer's disease is a progressive illness that affects more than 5.5 million people in the United States with no effective cure or treatment. Symptoms of the disease include declines in memory and speech abilities and increases in aggression and insomnia. Recent research suggests that NLP techniques can detect early cognitive decline as well as monitor the rate of decline over time. The processed data can be used in a smart home environment to enhance the level of home care for Alzheimer's patients. This paper proposes early-stage research in software engineering and natural language processing for quantifying and evaluating the patient's cognitive state to determine the required level of support in a smart home.

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CAL: A Smart Home Environment for Monitoring Cognitive Decline

Cited by 10 publications

References 24 publications

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report (Preprint)

3CAP: categorizing the cognitive capabilities of Alzheimer’s patients in a smart home environment

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