Chia-Tai Chan

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.

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.

Abnormality detection for improving elder's daily life independent

Since the dramatic demographic change makes it inevitable that rapid aging of the population is an unprecedented phenomenon in Taiwan. A growing social problem is supporting older adults who want to live independently in their own homes. It needs a health assistance system to make them independent living up to a higher age. Recently, technological advancements have spurred various ideas and innovations to assist the elders living independently. In this paper, we proposed a homecare sensory system that uses RFID-based sensor networks to collect elders daily activities and conducts the data into Hidden Markov model (HMM) and Support Vector Machines (SVMs) to estimate whether the elders behavior is abnormal or not. Through detecting and distinguishing the abnormal behaviors of elders daily activities, the system provides assistance on elders independent living and improvement of aged quality of life.

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.

A Resident's Behavior Simulation Model for Nursing Home Healthcare Services

The design of elaborate healthcare services is becoming increasingly difficult in todays nursing home. In this work, we develop a model that simulates the nursing home residents behavior to assist the facility planners in making fair and feasible management. Since a series of activities constituted one behavior, every activity is performed in a time interval and then starts next activity consequently. Whether the residents activity posed at any particular state, its future behavior is always having the same occurrence probability regardless of how it positioned in that state. This simulation model used exponential distribution to produce the stochastic activity interval for each activity. The activity transitions can be formalized through a randomized probability with respect to each activity frequency (AF). In the simulation model, the characteristics of nursing home residents activities can be generated to simulate a large scaled residents behavior and aid the facility managers in providing higher quality of healthcare services.

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.

Data Collection and Analysis Using Wearable Sensors for Monitoring Knee Range of Motion after Total Knee Arthroplasty

Total knee arthroplasty (TKA) is the most common treatment for degenerative osteoarthritis of that articulation. However, either in rehabilitation clinics or in hospital wards, the knee range of motion (ROM) can currently only be assessed using a goniometer. In order to provide continuous and objective measurements of knee ROM, we propose the use of wearable inertial sensors to record the knee ROM during the recovery progress. Digitalized and objective data can assist the surgeons to control the recovery status and flexibly adjust rehabilitation programs during the early acute inpatient stage. The more knee flexion ROM regained during the early inpatient period, the better the long-term knee recovery will be and the sooner early discharge can be achieved. The results of this work show that the proposed wearable sensor approach can provide an alternative for continuous monitoring and objective assessment of knee ROM recovery progress for TKA patients compared to the traditional goniometer measurements.

Activity Recognition and Activity Level Estimation for Context-Based Prompting System of Mild Cognitive Impairment Patients

The number of elderly people, who are unable to live independently and need assistance due to cognitive impairment, will rise rapidly in the aging society. To assist the independent living of these individuals and decrease the caregiver burden have become an important public health concern in the future. Mild Cognitive Impairment (MCI) is an intermediate state between normal cognitive function and dementia. The symptoms of MCI include difficulty remembering recent events or recently acquired information, depression and anxiety. MCI also increases the fall risk and affects patients’ social function and behavior. Sufficient physical activities can improve health of brain and reduce the risk of MCI. Since the context-aware computing technologies for assisting living have gained great popularity. We proposed a context-based activity prompting system to improve quality of life for MCI patients. The proposed system utilizes a smart phone as a sensor device to transmit sensing data to cloud server for activity recognition and activity level estimation, and uses context-based technique to provide activity prompting message to MCI patients. The activity prompting service supplies the activities self-management for MCI patients and helps them living independently. The system also provides real time fall detection mechanism to shorten the rescue time when accident happened. The experimental results have demonstrated that the proposed system achieves high accuracy on activity recognition and activity level estimation.

Design and Implementation of Teleconsultation System for Instant Treatment

Since the medical environment becomes more complicated nowadays, frequent exchange of information and cooperation among the professionals are the indispensable factors to enhance the quality of medical care. An efficient teleconsultation plays an important role for surgical emergency and medical decision making. The advances in the information communication technique during the past decade have already made the remote consulting feasible. However, the remote consulting must provide rapid response time, high quality medical images and flexible cooperation platform. Based on the combination of Computer-Supported Cooperative Work (CSCW) and Picture Archiving and Communication System (PACS), we propose an effective teleconsultation system to achieve the purpose of remote diagnosis support. The Internet virtual community concept is also adopted to provide a convenient and rapid connection manner for participants. Furthermore, the comparison results between proposed system and previous works demonstrate that it fulfills the requirements of remote teleconsultation system.