Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia

Ahamed, Farhad; Shahrestani, Seyed; Cheung, Hon

doi:10.3390/s20216031

Cited by 37 publications

(18 citation statements)

References 40 publications

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“…While there have been advances in detecting early neuropathology, it may be necessary to consider a shift in the diagnostic paradigm so that milder dementia can be detected earlier in order to obtain greater benefits from interventions [ 22 , 23 ]. Identifying the symptoms of the early stages of dementia is often difficult among older adults living in residential care [ 24 ]. It has been reported that more than 10% of community-dwelling subjects older than 70 years suffer from very mild or mild dementia [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

Hsiu

Lin

Weng

et al. 2022

Sensors

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Early identification of cognitive impairment would allow affected patients to receive care at earlier stage. Changes in the arterial stiffness have been identified as a prominent pathological feature of dementia. This study aimed to verify if applying machine-learning analysis to spectral indices of the arterial pulse waveform can be used to discriminate different cognitive conditions of community subjects. 3-min Radial arterial blood pressure waveform (BPW) signals were measured noninvasively in 123 subjects. Eight machine-learning algorithms were used to evaluate the following 4 pulse indices for 10 harmonics (total 40 BPW spectral indices): amplitude proportion and its coefficient of variation; phase angle and its standard deviation. Significant differences were noted in the spectral pulse indices between Alzheimer’s-disease patients and control subjects. Using them as training data (AUC = 70.32% by threefold cross-validation), a significant correlation (R2 = 0.36) was found between the prediction probability of the test data (comprising community subjects at two sites) and the Mini-Mental-State-Examination score. This finding illustrates possible physiological connection between arterial pulse transmission and cognitive function. The present findings from pulse-wave and machine-learning analyses may be useful for discriminating cognitive condition, and hence in the development of a user-friendly, noninvasive, and rapid method for the early screening of dementia.

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Section: Discussionmentioning

confidence: 99%

Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

Hsiu

Lin

Weng

et al. 2022

Sensors

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“…Meanwhile, some researchers used wearable sensors to estimate lower limb muscle strength [9] or evaluated the extent of frailty [10]. In addition, models for detecting danger pose [11] and anomalies of health data [12] were constructed in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Research on an Anomaly Detection Method for Physical Condition Change of Elderly People in Care Facilities

Shiotani

Yamaguchi

2022

ABE

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Currently, the shortage of care workers for the elderly has become a big problem, and more streamlined care operations are needed. In care facilities, care workers are required to use their subjective experience to detect anomalies in physical condition of care receivers, including serious or insigni cant deterioration or behavioral and psychological symptoms of dementia, which can decrease the work ef ciency. Therefore, we aim to create a model using objective data for detecting anomalies in physical condition. In this study, data from 13 subjects in a care facility were collected, and isolation forest models were constructed for each subject. The subjectʼs anomalies in physical condition were documented in a care record by a nurse and used as reference for model evaluation. Recall and speci city were used to evaluate the model, expressed as the percentage of detection success for abnormal or normal conditions. Data collected for 1 to 60 days were used to train the isolation models, and the relationship between the amount of training data and model performance was simulated. Heart rate, respiratory rate, and time of getting out of bed were collected from a sensor placed on the subjectʼs bed and used as the model features. In addition, dietary intake information was collected from the care record. Analysis of the evaluation results showed recall and speci city of 45.6 ± 46.7% and 83.88 ± 6.06%, respectively, for the model constructed using training data of 60 days. For future studies, we will continue to collect data and increase the number of participants to improve the robustness and accuracy of the proposed anomaly detection system.

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“…Daily activity patterns of people with dementia were analyzed by ML methods based on data recorded via environmental sensors [ 6 , 7 ]. A classification model for dementia was developed by extracting and selecting distinctive features based on the dynamic ranking based on a public dataset of daily life activities of adults in a smart home [ 8 ]. Outcomes for older adults in wearables and artificial intelligence (AI)–powered digital health community was improved [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Relationship between Natural Standing Behavior of Elderly People and a Class of Standing Aids in a Living Space

Miyazaki

Hirano

Kitamura

et al. 2022

Sensors

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As the world’s population ages, technology-based support for the elderly is becoming increasingly important. This study analyzes the relationship between natural standing behavior measured in a living space of elderly people and the classes of standing aids, as well as the physical and cognitive abilities contributing to household fall injury prevention. In total, 24 elderly standing behaviors from chairs, sofas, and nursing beds recorded in an RGB-D elderly behavior library were analyzed. The differences in standing behavior were analyzed by focusing on intrinsic and common standing aid characteristics among various seat types, including armrests of chairs or sofas and nursing bed handrails. The standing behaviors were categorized into two types: behaviors while leaning the trunk forward without using an armrest as a standing aid and those without leaning the trunk forward by using an arrest or handrail as a standing aid. The standing behavior clusters were distributed in a two-dimensional map based on the seat type rather than the physical or cognitive abilities. Therefore, to reduce the risk of falling, it would be necessary to implement a seat type that the elderly can unconsciously and naturally use as a standing aid even with impaired physical and cognitive abilities.

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Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia

Cited by 37 publications

References 40 publications

Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

Research on an Anomaly Detection Method for Physical Condition Change of Elderly People in Care Facilities

Analysis of Relationship between Natural Standing Behavior of Elderly People and a Class of Standing Aids in a Living Space

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