Alzheimer’s Disease Rehabilitation using Smartphones to Improve Patients’ Quality of Life

Zmily, Ahmad; Abu-Saymeh, Dirar

doi:10.4108/icst.pervasivehealth.2013.252248

Cited by 18 publications

(11 citation statements)

References 14 publications

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“…Currently, many applications related to fall detection [42], diagnosing Parkinson disease [43], and Alzheimer disease rehabilitation [44] have been designed using smartphones to improve patients' quality of life. These smartphones can also be used for identifying simple as well as complex activities [45].…”

Section: Related Workmentioning

confidence: 99%

Towards human activity recognition for ubiquitous health care using data from awaist-mounted smartphone

Zia¹,

Khalil²,

Khan³

et al. 2020

Turk J Elec Eng & Comp Sci

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Understanding human activities is a newly emerging paradigm that is greatly involved in developing ubiquitous health care (u-Health) systems. The aim of these systems is to seamlessly gather knowledge about the patient's health and, after collecting knowledge, make suggestions to the patient according to his/her health profile. For this purpose, one of the most important ubiquitous communication trends is the smartphone, which has drawn the attention of both professionals and caregivers for monitoring the aging population, childcare, fall detection, and cognitive impairment. Recognizing human actions in a ubiquitous environment is very challenging and researchers have extensively investigated different methods to recognize human activities in the past decade. However, this field of research still needs further exploration in order to improve the accuracy and reduce the computational cost of these health care systems. Therefore, for expediting the existing system, this research work investigated a novel approach based on feature selection and classification. In the proposed work, sparse Bayesian multinomial logistic regression (SBMLR) is used for feature subset selection and a multiclass support vector machine (SVM) is adapted for the classification of six human daily activities (laying down, walking up stairs, walking down stairs, sitting, standing, and walking). For identifying the best features among the features returned by the SBMLR, a tuned threshold value is used for the selection of the features. Further, other classification algorithms including K-nearest neighbor, decision tree, and naive Bayes and different feature selection methods such as principal component analysis and random subset feature selection are also used for evaluation and comparison. The dataset used for testing is obtained from the UCI Machine Learning Repository. It is collected by using a smartphone embedded with an accelerometer and gyroscope. The experimental results show that the highest accuracy of 99.40% can be achieved by using the proposed method. Moreover, the paired sample two-tailed t-test over the significance level of 0.05 reveals that the performance difference between the proposed technique and a competing technique is statistically significant.

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Section: Related Workmentioning

confidence: 99%

Towards human activity recognition for ubiquitous health care using data from awaist-mounted smartphone

Zia¹,

Khalil²,

Khan³

et al. 2020

Turk J Elec Eng & Comp Sci

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show abstract

“…The ontology was used to identify the challenging behaviors that arose more concerns to the caregivers. In [45], a smartphone-based application, called ADcope was introduced for integrating services such as the memory wallet, calendaring, Near Field Communication (NFC) enclosure content tagging and dementia exercises for AD patients.…”

Section: Related Workmentioning

confidence: 99%

Activity tracking and monitoring of patients with alzheimer’s disease

Lam

Tsang

Han

et al. 2015

Multimed Tools Appl

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In this paper, by applying motion detection and machine learning technologies, we have designed and developed an activity tracking and monitoring system, called SmartMind, to help Alzheimer's Disease (AD) patients to live independently within their living rooms while providing emergency assistances and supports when necessary. Allowing AD patients to handle their daily activities not only can release the burdens on their families and caregivers, it is also highly important to help them regain confidence towards a healthy life. The daily activities of a patient captured from SmartMind can also serve as an important indicator to describe his/her normal living habit (NLH). By checking NLH, the patient's current health status can be estimated on a daily basis. In the testing experiments of SmartMind, we have demonstrated the accuracy of SmartMind in activity detection and investigated its A preliminary version of the paper is appearred in Multimed Tools Appl performance when different machine learning algorithms were adopted for posture detection. The performance results indicate that both support vector machine (SVM) and naive bayes (NB) can achieve an accuracy of higher than 97 % while the random forrests (RF) only gives an accuracy of around 73 %.

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“…In [14], smart sensors are employed for monitoring human behavior and providing health information to help elderly to develop an active and independent living and for early detection of psychomotor abnormalities. In [23], a smartphone-based application, called ADcope is introduced for integrating services such as the memory wallet, calendaring, Near Field Communication (NFC) enclosure content tagging and dementia exercises for patients with AD. Since getting lost on streets is common to many AD patients, a non-intrusive pervasive model is proposed in [6] to assist patients using low-cost sensors to determine their current locations.…”

Section: Related Workmentioning

confidence: 99%

SmartMind: Activity Tracking and Monitoring for Patients with Alzheimer's Disease

Lam

Tsang

Han

et al. 2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

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In this paper, we introduce SmartMind, an activity tracking and monitoring system to help Alzheimer's diseases (AD) patients to live independently within their living rooms while providing emergent help and support when necessary. Allowing AD patients to handle their daily activities not only can release some of the burdens on their families and caregivers, but also is highly important to help them regain confidence towards a healthy life and reduce the degeneration rates of their memories. The daily activities of a patient captured from SmartMind can also serve as important indicators to describe his/her normal living habit (NLH). By checking with NLH, the patient's current health status can be estimated on a daily basis.

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Alzheimer’s Disease Rehabilitation using Smartphones to Improve Patients’ Quality of Life

Cited by 18 publications

References 14 publications

Towards human activity recognition for ubiquitous health care using data from awaist-mounted smartphone

Towards human activity recognition for ubiquitous health care using data from awaist-mounted smartphone

Activity tracking and monitoring of patients with alzheimer’s disease

SmartMind: Activity Tracking and Monitoring for Patients with Alzheimer's Disease

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