Detecting Dementia-Related Wandering Locomotion of Elders by Leveraging Active Infrared Sensors

Lin, Qiang; Zhao, Weichao; Wang, Weilan

doi:10.4236/jcc.2018.65008

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…Different schemes performed well and provided good results with their own limitations. In [47], they used infrared sensors and achieved 98% accuracy for path recognition. The grid-based layout scheme used for tracking in [53] and identification accuracy was 90%.…”

Section: Discussionmentioning

confidence: 99%

“…Wandering behaviors can be classified with the help of movement patterns. Several attempts were taken to define wandering behavior, and widely accepted definition still not visible because this behavior is very complex [47]. Algase [48] defined wandering behavior as a syndrome of dementia-related locomotion behavior which has frequent, repetitive and temporally-disorder and they can be illuminated in lapping, random or pacing patterns.…”

Section: A Wandering Patternsmentioning

confidence: 99%

“…This issue discussed as future work in the conclusion. Few prior studies design own methods for data processing such as in [47], they used an infrared sensor to track wandering behaviors and developed two algorithms to identify special patterns. In [51], they used GPS sensors and also used three algorithms to recognize paths because every behavior effects on angles.…”

Section: Data Processing and Path Recognitionmentioning

confidence: 99%

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Edge Computing-Based Localization Technique to Detecting Behavior of Dementia

et al. 2020

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Wireless localization systems have significant impact in the field of human-driven edge computing (HEC). It became very attractive among the researchers and used in applications of numerous areas such as medical, industrial, public safety, logistics, and so on. Ultra-wideband (UWB) technology used in localization systems owing to achieving high accuracy in real-time. In this paper, we exhibit a UWB based localization system based on the edge computing (EC) paradigm to analyze the wandering behavior of the patients who are suffering from dementia disease in the large-scale form. Physical changes in the brain are responsible for dementia disease. The appearance of wandering behavior is a common manner of the patients, which also a threat and interference for caregivers. We used the UWB standard appliance to symbolize various sorts of wandering patterns, including pacing, lapping, and two random movements in the large 2D map. The flow of all the movements illustrated in the X and Y-axis. Support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms used to classify all the patterns and accuracy result is above 99%. The result shows that the proposed system can achieve high accuracy in classification and satisfactory for applications in the medical area.

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Section: Discussionmentioning

confidence: 99%

Section: A Wandering Patternsmentioning

confidence: 99%

Section: Data Processing and Path Recognitionmentioning

confidence: 99%

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Edge Computing-Based Localization Technique to Detecting Behavior of Dementia

et al. 2020

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“…Vuong et al applied supervised machine learning methods for automatically classifying indoor trajectories according to the Martino-Saltzman model [28]. Lin et al proposed a method to identify repetitive locomotion episodes in the home according to well-known models of wandering [29]. Khodabandehloo and Riboni proposed a collaborative mining approach to assess the cognitive status of smarthome inhabitants based on statistical features extracted from their trajectories [7].…”

Section: Iot Techniques For Detecting Locomotion Anomaliesmentioning

confidence: 99%

TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

2021

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The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of the elderly. In this work, we investigate the use of sensor data and deep learning to recognize those patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduce novel visual feature extraction methods for locomotion data. Our solution relies on locomotion trace segmentation, image-based extraction of salient features from locomotion segments, and vision-based deep learning. We carried out extensive experiments with a large dataset acquired in a smart-home test bed from 153 seniors, including people with cognitive diseases. Results show that our system can accurately recognize the cognitive status of the senior, reaching a macro-$$F_1$$ F 1 score of 0.873 for the three categories that we target: cognitive health, mild cognitive impairment, and dementia. Moreover, an experimental comparison shows that our system outperforms state-of-the-art methods.

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“…Intervensi untuk mencegah wandering masih perlu ditingkatkan. Penanganan penderita demensia dan wandering dengan metode tradisional adalah dengan memberikan restraint dan atau terapi neuroleptik (Qiang, Weichao, and Weilan, 2018). Metode tradisional tersebut masih sangat jauh dari efektif karena memberikan efek samping yang lebih besar (Volicer, Van Der Steen and Frijters, 2013).…”

Section: Pendahuluanunclassified

PENGARUH SOCIAL LEISURE ACTIVITY TERHADAP PENURUNAN RISIKO WANDERING PADA LANSIA DEMENSIA DI RSJ Dr. RADJIMAN WEDIODININGRAT LAWANG MALANG

Kusumaningsih

Yusuf²,

Suhardiningsih³

2020

jikep

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Introduction: Dementia is a clinical syndrome of decline in cognitive function. Dementia with various neuropsychiatric symptoms can have a large impact on both clients and caregivers, one of which is wandering behavior. Wandering is a neuropsychiatric symptom that is difficult to control, challenging, at risk of loss, causes various risks of fracture, and even death. Wandering management still uses traditional methods until now. The purpose of this study was to determine the effect of Social Leisure Activity in reducing the risk of wandering. Methods: The design of this study was quasi-experimental with a pre-post test one group. The study population was 47 elderly and a sample of 34 elderly dementia along with their caregivers who were outpatients at the Psychogeriatric Clinic of Dr. RSJ Radjiman Wediodiningrat Lawang. The sampling technique used purposive sampling method. The independent variable in this study was the Social Leisure Activity and the dependent variable was wandering risk. The instrument used Elopement Screening to assess the risk of wandering, the KATZ Independence Index, and the Caregiver Training questionnaire. The intervention was carried out at home by the caregiver in the form of a series of activities carried out for 4 weeks with a frequency of 3 times a week and 45 minutes per activity. The caregiver was given training as well as a demonstration of wandering and social leisure activity before the research was conducted. Data were analyzed using Paired-Samples T-Test. Results: The results of the analysis showed that there were significant differences (p = 0,000) on the value of the risk of wandering before and after an intervention. Training data on the caregiver gave a result that there was a significant difference (p = 0,000) in the caregiver's knowledge before and after training. Discussion: The risk of wandering in dementia elderly could be reduced by providing regular physical activities that prioritize elements of social interaction. Social Leisure Activity is an effective activity to be developed because it is easy to implement and efficient because it does not require high costs. Conclusions and Suggestions: Social Leisure Activities can help reduce the risk of wandering if done regularly and continuously. Social Leisure Activity can be used as an alternative for caregivers and elderly dementia to keep on doing activities.

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Detecting Dementia-Related Wandering Locomotion of Elders by Leveraging Active Infrared Sensors

Cited by 13 publications

References 20 publications

Edge Computing-Based Localization Technique to Detecting Behavior of Dementia

Edge Computing-Based Localization Technique to Detecting Behavior of Dementia

TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes

PENGARUH SOCIAL LEISURE ACTIVITY TERHADAP PENURUNAN RISIKO WANDERING PADA LANSIA DEMENSIA DI RSJ Dr. RADJIMAN WEDIODININGRAT LAWANG MALANG

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