Chih-Ning Huang

A Reliable Transmission Protocol for ZigBee-Based Wireless Patient Monitoring

Patient monitoring systems are gaining their importance as the fast-growing global elderly population increases demands for caretaking. These systems use wireless technologies to transmit vital signs for medical evaluation. In a multihop ZigBee network, the existing systems usually use broadcast or multicast schemes to increase the reliability of signals transmission; however, both the schemes lead to significantly higher network traffic and end-to-end transmission delay. In this paper, we present a reliable transmission protocol based on anycast routing for wireless patient monitoring. Our scheme automatically selects the closest data receiver in an anycast group as a destination to reduce the transmission latency as well as the control overhead. The new protocol also shortens the latency of path recovery by initiating route recovery from the intermediate routers of the original path. On the basis of a reliable transmission scheme, we implement a ZigBee device for fall monitoring, which integrates fall detection, indoor positioning, and ECG monitoring. When the triaxial accelerometer of the device detects a fall, the current position of the patient is transmitted to an emergency center through a ZigBee network. In order to clarify the situation of the fallen patient, 4-s ECG signals are also transmitted. Our transmission scheme ensures the successful transmission of these critical messages. The experimental results show that our scheme is fast and reliable. We also demonstrate that our devices can seamlessly integrate with the next generation technology of wireless wide area network, worldwide interoperability for microwave access, to achieve real-time patient monitoring.

Development of a Fall Detecting System for the Elderly Residents

Accidental falls are common causes of serious injury and health threats in the elder population. To deliver adequate medical support, the robust and immediate falls detection is important. Since the fall detection in the elderly remains a major challenge in the public health domain, effective fall-detection will provide urgent support and dramatically reduce the cost of medical care. In this work, we propose a fall-detecting system placing an accelerometer on the head level and using an algorithm to distinguish between falls and daily activities. The experimental results have demonstrated the proposed scheme with high reliability and sensitivity on fall detection. The system is not only cost effectively but also potable. It fulfills the requirements of fall detection.

A reliable fall detection system based on wearable sensor and signal magnitude area for elderly residents

Falls are the primary cause of accidents for elderly people and often result in serious injury and health threats. It is also the main obstacle to independent living for frail and elderly people. A reliable fall detector can reduce the fear of falling and provide the user with the reassurance to maintain an independent lifestyle since the reliable and effective fall detection mechanism will provide urgent medical support and dramatically reduce the cost of medical care. In this work, we propose a fall-detecting system based on a wearable sensor and a real-time fall detection algorithm. We use a waist- mounted tri-axial accelerometer to capture movement data of the human body, and propose a fall detection method that uses the area under a signal magnitude curve to distinguish between falls and daily activities. Experimental results demonstrate the effectiveness of proposed scheme with high reliability and sensitivity on fall detection. The system is not only cost effective but also portable that fulfills the requirements of fall detection.

Location-Aware Fall Detection System for Medical Care Quality Improvement

Abstract—Falls are one of the most common adverse events in hospitals and fall management remains a major challenge in the medical care quality. Falls in patients are associated with major health complications that can result in health decline and increased medical care cost. To deliver medical care in time, reliable location-aware fall detection is needed. In this paper, we propose a patient alert alarm for fall management. It is ZigBee-Based location awareness fall detection system that provides immediate position information to the caregivers as soon as the fall happened. Obviously, the integration of location awareness and fall detection technologies fulfills the requirements of delivering critical information to relative professions and improve the medical care quality.

Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model

Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences.

An Intelligent RFID System for Improving Elderly Daily Life Independent in Indoor Environment

Since elderly obliviousness causes social inconvenience and psychical complaint, elders often forget daily schedules and miss their personal belongings such pillboxes or keys. Recently, technological advancements have spurred various ideas and innovations to apply on elder independent living. In this article, we proposed an intelligent RFID (Radio Frequency Identification) system to assist elder living independent and improve aged quality of life.

A Rehabilitation Exercise Assessment System Based on Wearable Sensors for Knee Osteoarthritis

In order to enable the knee OsteoArthritis (OA) patients to manage their own rehabilitation progress, this study develops a rehabilitation exercise assessment system for knee OA using three wearable sensors mounted on the chest, thigh and shank of working leg. The system derives 51 features from the calculated angles, spectrum, and means of the acceleration signals to judge the exercise type and determine whether their postures are correct or not. After ten subjects performed three kinds of rehabilitation activities, we got 99.29% accuracy for exercise type classification, and 90.14% accuracy for wrong exercise recognition. The experimental results demonstrate that the proposed system can help the physician and patients to monitor the rehabilitation progress efficiently.

A machine learning approach to fall detection algorithm using wearable sensor

Falls are the primary cause of accidents for the elderly in living environment. Falls frequently cause fatal and non-fatal injuries that are associated with a large amount of medical costs. Reduction hazards in living environment and doing exercise for training balance and muscle are the common strategies for fall prevention. But falls cannot be avoided completely; fall detection provides the alarm in time that can decrease the injuries or death caused by no rescue. We propose machine learning-based fall detection algorithm using multi-SVM with linear, quadratic or polynomial kernel function, and k-NN classifier. Eight kinds of falling postures and seven types of daily activities arranged in the experiment are used to explore the performance of the machine learning-based fall detection algorithm. The emulated falls were performed on a soft mat by ten healthy young subjects wearing protectors. The k-nearest neighbor method with 0.1 second window size has the highest accuracy, which is 96.26%. The results show that the proposed machine learning fall detection algorithm can fulfill the requirements of adaptability and flexibility for the individual differences.

Location-awareness Communications System for Improving Medical Service Quality

Location-awareness computing enables services based on the current location of a user. By adding a layer of location-awareness intelligence to the VoWLAN platform, a location-enabled VoWLAN service may further bring seamless connections for medical professionals to communicate and collaborate with each other efficiently. It significantly aids the locating relative professionals and delivering critical information for hospital urgencies. In this article, we proposed a Location Aware Patient Care Communication System (LAPC2S) is the convergence of voice and data for the purpose of reducing medical errors and providing better hospital quality of service.

A Miniaturized PSK Demodulation Device for Short-Range Wireless Neural Prosthetic Systems

Neural prostheses have been extensively studied over the past few decades. For the applications requiring that the device be implanted into the body, all connectivities regarding the prosthetic system should be made wirelessly in order to achieve a permanent and minimally invasive implantation. Demodulator plays a key role in data integrity and overall power efficiency of the system. In this paper, an extremely efficient phase-shift keying demodulator (PSKD) family, termed quasi-coherent PSKD, is presented for the short-range wireless prosthetic systems. It has been fabricated in a 0.18-mum CMOS process and thoroughly tested. The overall design philosophy is described, and proof- of-concept results demonstrating the feasibility of the proposed design are given.