Alzh-care: - Knowledge - driven intelligent system to assist Alzheimer Patients

Rashmi S R, S. Suha, R. Krishnan, D. R. Ramesh Babu
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引用次数: 1

Abstract

Alzh-care is an Alzheimer patients assistive system. This system assists the patients by recognizing their intention on the activity and guides them to the completion of that task. It helps the patients in completing their day-today tasks by recognizing their intent of the task at the early stage. In this work, Intention recognition is achieved through the sensor based activity recognition process. These kinds of systems have far reaching applications in the domains like assisted living; healthcare monitoring and they offer lots of open challenges to solve in the arena of research and development.This paper discusses the implementation of the Alzh-care assistive system using sensors for the day-to-day activities like eating meals and taking medicines etc., We have used knowledge driven approach for representing the domain knowledge on the day-to-day activities is as ontology. This in turn is referred to know the intentions of the patients and voice assist them in completing the intended task. We have tried state machine approach to infer and assist on the task. Knowledge driven approach has proven to perform better than the data driven approaches as the latter suffers from the cold start and reliability issues.We have tabulated the trials and noted the success rate of recognition of the intent for each of the activity experimented. This system is proven to be 85% to 90% accurate for predicting the intentions in the finite disambiguous tasks, owing the other 5% to 10% of the failure caused by the sensors used.
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老年痴呆症护理:-知识驱动的智能系统,以帮助老年痴呆症患者
老年痴呆症护理是老年痴呆症患者的辅助系统。该系统通过识别患者在活动中的意图并指导他们完成任务来帮助患者。它通过在早期阶段识别患者的任务意图来帮助他们完成日常任务。在这项工作中,意图识别是通过基于传感器的活动识别过程来实现的。这类系统在辅助生活等领域有着深远的应用;医疗保健监控和它们在研究和开发领域提供了许多需要解决的开放挑战。本文讨论了利用传感器实现老年痴呆症辅助系统的日常活动,如吃饭和吃药等,我们使用知识驱动的方法将领域知识表示为日常活动的本体。这反过来又涉及到了解患者的意图和语音协助他们完成预期的任务。我们尝试了状态机方法来推断和辅助任务。事实证明,知识驱动的方法比数据驱动的方法性能更好,因为数据驱动的方法存在冷启动和可靠性问题。我们已将试验制成表格,并注意到每个实验活动的意图识别成功率。事实证明,该系统在有限的明确任务中预测意图的准确率为85%至90%,其余5%至10%的故障是由所使用的传感器引起的。
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