Preliminary results of using inertial sensors to detect dementia- related wandering patterns

Vuong, Nhu Khue; Chan, Syin; Lau, Chiew Tong; Yap, Philip; Chen, A.S.H.

doi:10.1109/embc.2015.7319197

Cited by 22 publications

(21 citation statements)

References 7 publications

(11 reference statements)

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“…The exclusion criterion is that the patient has other neurological disabilities or has significantly reduced ability to complete instrumental activities of daily living due to other non-dementia conditions. Informed and written consent shall be obtained from both the participant and their caregiver for both of their participations, however if the participant is incapable of consenting, their Next of Kin (NoK) or Power of Attorney (POA) will be asked to provide written and informed consent for them, with verbal assent being sought from the participant [11,13,14,[33][34][35][36][37]. All consenting and assenting parties will be informed they can withdraw from the study at any time without being required to give a reason or justification.…”

Section: Data Collection Experimentsmentioning

confidence: 99%

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

Harper

Ghali

Hussain

et al. 2021

Intelligent Computing Theories and Application

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Dementia is a neurodegenerative disease which leads to the individual experiencing difficulties in their daily lives. Often these difficulties cause a large amount of stress, frustration and upset in the individual, however identifying when the difficulties are occurring or beginning can be difficult for caregivers, until the difficulty has caused problematic behavior or undeniable difficulty to the person with dementia. Therefore, a system for identifying the onset of dementia-related difficulties would be helpful in the management of dementia. Previous work highlighted wearable computing-based systems for analyzing physiological data as particularly promising. In this paper, we outline the methodology used to perform a systematic search for a relevant dataset. However, no such dataset was found. As such, a methodology for collecting such a dataset and making it publicly available is proposed, as well as for using it to train classification models that can predict difficulties from the physiological data. Several solutions to overcome the lack of available data are identified and discussed: data collection experiments to collect novel datasets; anonymization and pseudonymization to remove all identifiable data from the dataset; and synthetic data generation to produce a larger, anonymous training dataset. In conclusion, a combination of all the identified methods should ideally be employed in future solutions. Future work should focus on the conductance of the proposed experiment and the sharing of the collected data in the manner proposed, with data ideally being collected from as many people as possible with as many different types of dementia as possible.

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Section: Data Collection Experimentsmentioning

confidence: 99%

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

Harper

Ghali

Hussain

et al. 2021

Intelligent Computing Theories and Application

View full text Add to dashboard Cite

show abstract

“…Vuong et al [45] use inertial sensor signals to distinguish the four wandering patterns [21] in movement. An accelerometer is utilized to calculate the acceleration of the patient to detect locomotion.…”

Section: H Patterns From Inertial Sensor Outputmentioning

confidence: 99%

A Survey on Wandering Behavior Management Systems for Individuals with Dementia

Hassan

Khan²

2019

ijacsa

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Alzheimer's and related dementia are associated with a gradual decline in cognitive abilities of an individual, impairing independent living abilities. Wandering, a purposeless disoriented locomotion tendency or behavior of dementia patients, requires constant caregiver supervision to reduce the risk of physical harm to patients. Integrating technology into care ecology has the potential to alleviate stress and expense. An automatic wandering detection system integrated with an intervention module may provide warnings and assistive suggestions in times of abnormal behavior. In this study, we survey existing research on technology aided methodologies and algorithms used in detection and management of wandering behavior of individuals affected with dementia. Our study provides insights into mechanisms of collecting movement data and finding patterns that distinguish wandering from normal behavior.

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“…Inertial sensors are utilized in Voung et al [3] to monitor the indoor wandering patterns to detect behavior disorder for people with dementia. Their preliminary experimental results have shown that their proposed solution has outperformed the existing classification algorithms based on time-series analysis with respect to accuracy and time performance.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Indoor Activity of Daily Living using Thermal Imaging: A Case Study

Ahmed¹,

Abdulrazak²

2021

IJACSA

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Monitoring indoor activities of daily living (ADLs) of a person is subjected to dependency on sensor type, power supply stability, and connectivity stability without mentioning artifacts introduced by the person himself. Multiple challenges have to be overcome in this field, such as; detecting the precise spatial location of the person, and estimating vital signs like an individual's average temperature. Privacy is another domain of the problem to be thought of with care. Identifying the person's posture without a camera is another challenge. Posture identification is a key in assisting detection of a person's fall. Thermal imaging could be a proper solution for most of the mentioned challenges. It provides monitoring both the person's average temperature and spatial location while maintaining privacy. In this research, an IoT system for monitoring an indoor ADL using thermal sensor array (TSA) is proposed. Three classes of ADLs are introduced, which are daily activity, sleeping activity and no-activity respectively. Estimating person average temperature using TSAs is introduced as well in this paper. Results have shown that the three activity classes can be identified as well as the person's average temperature during day and night. The person's spatial location can be determined while his/her privacy is maintained as well.

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Preliminary results of using inertial sensors to detect dementia- related wandering patterns

Abstract: We present a solution for detecting dementia-related travel patterns using only inertial sensors. The results and lessons learnt from the experiments on dementia and non-dementia subjects are reported.

Cited by 22 publications

References 7 publications

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

A Survey on Wandering Behavior Management Systems for Individuals with Dementia

Monitoring Indoor Activity of Daily Living using Thermal Imaging: A Case Study

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