Activity Recognition for Assisting People with Dementia

Fikry, Muhammad; Hamdhana, Defry; Lago, Paula; Inoue, Sozo

doi:10.1007/978-3-030-68590-4_10

Cited by 4 publications

(2 citation statements)

References 96 publications

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“…Smart and IoTbased technologies are improving the living style of dementia people and can ensure their safety during daily activities. Author in [20] summarized information about activity recognition of dementia individuals using machine learning methods. Tey merged sensor devices and smart devices during the monitoring process and used warning alarms to prevent abnormal activities in dementia people.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Cognitive Health Assessment Based on Daily Life Functional Activities

Alsubai

Alqahtani

Sha

et al. 2023

Computational Intelligence and Neuroscience

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Dementia is increasing day-by-day in older adults. Many of them are spending their life joyfully due to smart home technologies. Smart homes contain several smart devices which can support living at home. Automated assessment of smart home residents is a significant aspect of smart home technology. Detecting dementia in older adults in the early stage is the basic need of this time. Existing technologies can detect dementia timely but lacks performance. In this paper, we proposed an automated cognitive health assessment approach using machines and deep learning based on daily life activities. To validate our approach, we use CASAS publicly available daily life activities dataset for experiments where residents perform their routine activities in a smart home. We use four machine learning algorithms: decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP). Furthermore, we use deep neural network (DNN) for healthy and dementia classification. Experiments reveal the 96 % accuracy using the MLP classifier. This study suggests using machine learning classifiers for better dementia detection, specifically for the dataset which contains real-world data.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Cognitive Health Assessment Based on Daily Life Functional Activities

Alsubai

Alqahtani

Sha

et al. 2023

Computational Intelligence and Neuroscience

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“…Technologies have been developed to support their daily life, work, study, or research, such as personal web libraries [ 2 ], Tiimo [ 3 ], T-BOT and Q-BOT [ 3 , 4 ]. But people with very busy schedules, such as nurses, caregivers, or people with problems of cognition, loss of memory, or dementia [ 5 ] can forget the schedule that has been arranged, so a reminder system is required in this area [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Forecasted Activity Notifications with Reinforcement Learning

Fikry

Inoue

2023

Sensors

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In this paper, we propose the notification optimization method by providing multiple alternative times as a reminder for a forecasted activity with and without probabilistic considerations for the activity that needs to be completed and needs notification. It is important to consider various factors when sending notifications to people after obtaining the results of the forecasted activity. We should not send notifications only when we have forecasted results because future daily activities are unpredictable. Therefore, it is important to strike a balance between providing useful reminders and avoiding excessive interruptions, especially for low probabilities of forecasted activity. Our study investigates the impact of the low probability of forecasted activity and optimizes the notification time with reinforcement learning. We also show the gaps between forecasted activities that are useful for self-improvement by people for the balance of important tasks, such as tasks completed as planned and additional tasks to be completed. For evaluation, we utilize two datasets: the existing dataset and data we collected in the field with the technology we have developed. In the data collection, we have 23 activities from six participants. To evaluate the effectiveness of these approaches, we assess the percentage of positive responses, user response rate, and response duration as performance criteria. Our proposed method provides a more effective way to optimize notifications. By incorporating the probability level of activity that needs to be done and needs notification into the state, we achieve a better response rate than the baseline, with the advantage of reaching 27.15%, as well as than the other criteria, which are also improved by using probability.

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