BackgroundAlzheimer's disease (AD) is an irreversible brain disease that slowly destroys memory and thinking skills, and eventually the ability to carry out the simplest daily tasks. Recent studies showed that people with AD might actually benefit from physical exercises and rehabilitation processes. Studies show that rehabilitation would also add value in making the day for an individual with AD a little less foggy, frustrating, isolated, and stressful for as long as possible.ObjectiveThe focus of our work was to explore the use of modern mobile technology to enable people with AD to improve their abilities to perform activities of daily living, and hence to promote independence and participation in social activities. Our work also aimed at reducing the burden on caregivers by increasing the AD patients’ sense of competence and ability to handle behavior problems.MethodsWe developed ADcope, an integrated app that includes several modules that targeted individuals with AD, using mobile devices. We have developed two different user interfaces: text-based and graphic-based. To evaluate the usability of the app, 10 participants with early stages of AD were asked to run the two user interfaces of the spaced retrieval memory exercise using a tablet mobile device.ResultsWe selected 10 participants with early stages of AD (average age: 75 years; 6/10, 60% males, 4/10, 40% females). The average elapsed time per question between the text-based task (14.04 seconds) and the graphic-based task (12.89 seconds) was significantly different (P=.047). There was also a significant difference (P<.001) between the average correct answer score between the text-based task (7.60/10) and the graphic-based task (8.30/10), and between the text-based task (31.50/100) and the graphic-based task (27.20/100; P<.001). Correlation analysis for the graphic-based task showed that the average elapsed time per question and the workload score were negatively correlated (−.93, and −.79, respectively) to the participants’ performance (P<.001 and P=.006, respectively).ConclusionsWe found that people with early stages of AD used mobile devices successfully without any prior experience in using such devices. Participants’ measured workload scores were low and posttask satisfaction in fulfilling the required task was conceivable. Results indicate better performance, less workload, and better response time for the graphic-based task compared with the text-based task.

Alzheimer's Disease (AD) is an incurable disease that causes dementia. Rehabilitation efforts of this disease focus on slowing down the rate of progression and improving the quality of life of the patients by enhancing their ability to engage with the environment and society surrounding them. In this paper, we present an integrated application (ADcope) that utilizes mobility and advanced communication features of smartphones to rehabilitate AD patients. ADcope integrates quality of life enhancing modules such as the memory wallet, calendaring,and NFC enclosure content tagging, and dementia exercising modules that incorporate audio assisted memory training and spaced retrieval exercises. Initial trials of the ADcope application with AD patients confirm that the benefits of previously proposed AD tools and exercises can also be achieved using a smartphone application. The simplicity of using the ADcope application can increase the rate of adoption of AD tools in dealing with AD patients.978-1-936968-80-0

Falls are a common cause of injuries and traumas for elderly and could be life threatening. Delivering a prompt medical support after a fall is essential to prevent lasting injuries. Therefore, effective fall detection could provide urgent support and dramatically reduce the risk of such mishaps. In this paper, we propose a hierarchical classification framework based on a novel anatomical-plane-based representation for elderly fall detection. The framework obtains human skeletal joints, using Microsoft Kinect sensors, and transforms them to a human representation. The representation is then utilized to classify the sensor input sequences and provide a semantic meaning of different human activities. Evaluation results of the proposed framework, using real case scenarios, demonstrate the efficacy of the framework in providing a feasible approach towards accurately detecting elderly falls.

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Smart phones equipped with Near Field Communication (NFC) provide a simple way to initiate contactless transactions and data exchange without having the need to carry additional items such as credit cards, personal IDs, and access keys. To prevent unauthorized NFC transactions in the case of lost or stolen devices, the user needs to be authenticated before each transaction, which adds extra burden on users. In this paper we propose an NFC security framework that simplifies the initiation of secure NFC transactions. The framework calculates a current measure of device security based on user activities and behavior. NFC transactions are authorized if the current device security measure meets the minimum requirement of the application. The framework uses a combination of authentication methods such as password, pin, pattern, finger print, voice and face recognition. In addition, we propose adjusting the device security level dynamically based on user activities, behavior, and background face and voice authentication. As a case study, the framework has been implemented on the Google Android platform. The NFC security framework minimizes the need to intrusively authenticate the user for every NFC transaction thus maintaining the simplicity of using NFC while enhancing its security.

Alzheimer's Disease (AD) is an incurable disease that causes dementia. Rehabilitation efforts of this disease focus on slowing down the rate of progression and improving the quality of life of the patients by enhancing their ability to engage with the environment and society surrounding them. In this paper, we present an integrated application (ADcope) that utilizes mobility and advanced communication features of smartphones to rehabilitate AD patients. ADcope integrates quality of life enhancing modules such as the memory wallet, calendaring, and NFC enclosure content tagging, and dementia exercising modules that incorporate audio assisted memory training and spaced retrieval exercises. Initial trials of the ADcope application with AD patients confirm that the benefits of previously proposed AD tools and exercises can also be achieved using a smartphone application. The simplicity of using the ADcope application can increase the rate of adoption of AD tools in dealing with AD patients.

As converged services are becoming the standard for corporate networks, concerns have been arising on the best way to ensure efficient use of expensive WAN bandwidth. Achieving excellent quality of multimedia and voice communication requires the allocation of large bandwidth for these services. In most cases, lossy compression codecs are used to reduce bandwidth requirements resulting in lower quality of service. The codec selection is maintained even during low bandwidth utilization. In this paper, we present a codec interface that combines high-fidelity high-bitrate and low-fidelity low-bitrate codecs. Audio packets from the low-bitrate codec are transmitted on the highest priority class of service of differentiated services networks. This ensures that these packets always arrive at destination. Audio packets from the high-bitrate codec are transmitted on the lowest priority class of service. At low bandwidth utilization, the high-fidelity high-bitrate packets will be used and during congestions, these packets would be dropped. This ensures achieving the best possible voice quality during varying network loads. Simulation results measuring the mean opinion score of voice quality show that superior voice quality can be achieved with this method over using standalone codecs while maintaining excellent quality of service for data traffic as well.

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