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 %.

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.

In this paper, we propose a novelpervasive business modelfor sales promotion in retail chain stores utilizing WLAN localization andnear field communication (NFC)technologies. The objectives of the model are to increase the customers’flowof the stores and theirincentivesin purchasing. In the proposed model, the NFC technology is used as the first mean to motivate customers to come to the stores. Then, with the use of WLAN, the movements of the customers, who are carrying smartphones, within the stores are captured and maintained in themovement database. By interpreting the movements of customers as indicators of their interests to the displayed items,personalizedpromotion strategies can be formulated to increase their incentives for purchasing future items. Various issues in the application of the adopted localization scheme for locating customers in a store are discussed. To facilitate the item management and space utilization in displaying the items, we propose anenhanced R-treefor indexing the data items maintained in the movement database. Experimental results have demonstrated the effectiveness of the adopted localization scheme in supporting the proposed model.

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Two of the major considerations in helping patients with Alzheimer's disease are: (1) the monitoring of patients' activities to minimize the risk in their daily lives and (2) to reduce the worsening rate of the symptoms of Alzheimer's disease. By introducing our tracking and monitoring system, SmartMind, the authors demonstrate how the latest pervasive and sensing technologies can help the patients living alone while providing immediate assistance if necessary. In addition, patients' current health statuses can be estimated daily by checking with the Normal Living Habit (NLH) recorded by the SmartMind. Since patients with Alzheimer's disease may result in serious mood problems, it is important to monitor their mood status. SmartMood, which works with SmartMind, provides estimation on the mood status of a patient by analysing his/her voice data captured from his/her smartphone while he/she is talking with others. Alerts are sent when an abnormal mood status is detected.