Modelling Reminder System for Dementia by Reinforcement Learning

Fikry, Muhammad; Mairittha, Nattaya; Inoue, Sozo

doi:10.1007/978-981-19-0361-8_9

Cited by 4 publications

(7 citation statements)

References 27 publications

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“…Both traditional ML and DL algorithms can fall into one of four learning types: supervised, unsupervised, semi‐supervised, and reinforcement learning. In this section, we briefly discuss each type of learning technique and the scope of their applicability to dementia research, except the use of reinforcement learning for dementia, which remains a relatively unexplored research area 14,15 …”

Section: Types Of ML Techniques Used In Dementia Researchmentioning

confidence: 99%

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Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

Alzheimer's & Dementia

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Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well‐documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI‐enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co‐produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care.HIGHLIGHTS Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

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Section: Types Of ML Techniques Used In Dementia Researchmentioning

confidence: 99%

“…In this section, we briefly discuss each type of learning technique and the scope of their applicability to dementia research, except the use of reinforcement learning for dementia, which remains a relatively unexplored research area. 14,15…”

Section: Types Of ML Techniques Used In Dementia Researchmentioning

confidence: 99%

Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

Alzheimer's & Dementia

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“…In daily activities, notifications can be utilized as reminders for specific events [ 38 , 39 ], location-based alerts [ 40 , 41 , 42 , 43 ], and activity prompts [ 6 , 42 , 43 , 44 ]. The importance of notifications has led to an increasing demand for user attention on smartphones, as notifications serve as the primary means of conveying information to the intended recipients.…”

Section: Related Workmentioning

confidence: 99%

“…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%

“…Through the use of reminders, it is possible to facilitate the recollection of vital information. Some reminder systems notify tasks based on a predetermined time and then send notifications according to this plan [ 6 ]. People can set their own reminder system according to their preferences, but they cannot be aware of impending activities; many things can occur outside of their schedule due to busyness [ 10 ], stress [ 11 ], perspective memory [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Garcia,

Inoue

2023

Preprint

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In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff to patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of sensor data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different sensors by relabeling to the sensors with less samples resolving data imbalance. Standard deviation and Kullback-Leibler divergence between minority and majority class are used to measure signal pattern to find matching sensors to relabel. By matching sensors between classes, two variation of relabeling are implemented specifically full and partial matching. The performance is evaluated using real world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the sensor data with our proposed relabeling method for data augmentation, we achieved higher minority class F1-score as compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected sensor data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

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Modelling Reminder System for Dementia by Reinforcement Learning

Cited by 4 publications

References 27 publications

Artificial intelligence for dementia research methods optimization

Artificial intelligence for dementia research methods optimization

Optimizing Forecasted Activity Notifications with Reinforcement Learning

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

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