Wen-Cheng Chou

Design and Assessment of a Real-Time Accelerometer-Based Lying-to-Sit Sensing System for Bed Fall Prevention

Bed falling is an important issue to the hospital. However, it seems that using bedrail restraints or bed alarm systems cannot succeed in preventing bed falls in hospital. Moreover, the bed alarm systems are too expensive for individuals who choose to rest at home due to lack of medical care resource nowadays. In this work, we design a low cost and real-time lying-to-sit sensing system with accelerometer attached on the chest. The system implements a proposed intelligent and low complexity tilt sensing algorithm to calculate the tilting angle of the upper body in real-time and standalone fashion. It can detect those people with high falling-risk when trying to sit up or getting out of beds and send alarms to medical care personnel. Such that, they can receive appropriate care and support immediately. As a result, the bed falls can be prevented on those people.

Development of a Wearable Instrumented Vest for Posture Monitoring and System Usability Verification Based on the Technology Acceptance Model

Body posture and activity are important indices for assessing health and quality of life, especially for elderly people. Therefore, an easily wearable device or instrumented garment would be valuable for monitoring elderly people’s postures and activities to facilitate healthy aging. In particular, such devices should be accepted by elderly people so that they are willing to wear it all the time. This paper presents the design and development of a novel, textile-based, intelligent wearable vest for real-time posture monitoring and emergency warnings. The vest provides a highly portable and low-cost solution that can be used both indoors and outdoors in order to provide long-term care at home, including health promotion, healthy aging assessments, and health abnormality alerts. The usability of the system was verified using a technology acceptance model-based study of 50 elderly people. The results indicated that although elderly people are anxious about some newly developed wearable technologies, they look forward to wearing this instrumented posture-monitoring vest in the future.

The design and development of a wearable posture monitoring vest

This study presents the design and development of a new textile based intelligent wearable vest for real-time multi-posture monitoring and warning of urgency through the cooperation of the Engineering School at Chang Gung University and Taiwan Textile Research Institute (TTRI). With the system, it offers a high portability, low cost, and indoor/outdoor solution suitable for long-term health care usage at home for health promotion, healthy aging assessment, and health abnormality alert about the human postures.

The design of an in-line accelerometer-based inclination sensing system

In this paper, we present a novel inclination sensing system, which is composed of an inexpensive and low power microcontroller with a 3-Axis digital accelerometer. Accelerometers have been widely used to determine the inclination or tilt angles of a system by calculating angles between gravity vector and its three axes. However, the angle calculation is so complicated that in most previous research work it is usually finished off-line on a more powerful computation resource, such as a PC/PDA or implemented with expensive memory lookup-tables. In the proposed system, we design and implement a novel algorithm on the embedded microcontroller such that the system is capable of transferring the raw data of the accelerometer from motion domain to angular domain in-line, such that the system can provide the inclination or tilt angle information alone in real-time.

A real-time gesture recognition implementation on SoC development platform

This paper proposes a real-time gesture recognition algorithm, describes the implementation of it on a SoC development platform, and shows its application. In our design, we adopt the vision-based approach to identify the gestures. The images are retrieved from webcam at the rate of 30 fps, and then go through the pre-processing, and noise cancelation phases. After that, we use template matching to identify what the gestures are. Finally, we combine with the application of digital photo frame and demonstrate using our gesture recognition system in real-time to control the digital photo frame which is operated by touch-screen previously.

A novel accelerometer-based method for the real-time assessment of Parkinson's tremor

Parkinsons disease is the second most common neurodegenerative disease. Tremor is an early symptom for about 70% of patients with Parkinsons disease. Accelerometers have been widely used for the detecting of the tremor signals. There were also several signal processing methods have been proposed to assess and quantify the tremor behavior of the Parkinsons disease, such as the power spectral density and wavelet analysis, etc.. In this paper, we present a novel method, suitable for the real-time implementation of assessing Parkinsons tremor, based on a low-cost accelerometer sensing system.

A digital tilt converter for three-axis accelerometers

Three-axis accelerometers have been widely used not only in motion sensing but also in tilting sensing nowadays. The tilting angle conversions from 3D acceleration data involves with complicated mathematic calculations such as multiplication, division, square-root and trigonometric functions. Indeed, the conversion can be done by only using simple addition, subtraction, and shifting operations through our proposed 2DCORDIC based tilt sensing algorithm. In this work, a hardware framework of this algorithm is realized, validated by FPGA development board, and tested through I2C interface with a microcontroller and a 3-axis digital accelerometer.

The design of a real-time accelerometer-based sleeping position monitoring system and its application on obstructive sleep apnea syndrome

In this paper, we design and present a novel real-time sleeping position monitoring system. The sensing modules composed of 3-axis accelerometers are placed on objects forehead and chest to monitor the objects position during sleeping by calculating the angles between gravity vector and its three axes. System is driven by an inexpensive and low power microcontroller. In this system, we implement a proposed novel CORDIC-based algorithm on the embedded microcontroller so that the system is capable of transferring the raw data of the accelerometer from motion domain to angular domain in-line, and the system can provide the inclination or tilt angle information by itself in real-time. The sleeping position information is integrated into polysomnography (PSG) to cooperate the study of obstructive sleep apnea (OSA) syndrome.

Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG

In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature values from the CHAMP system have been correlated with myo-cardiac functions obtained from actual heart failure (HF) patients. The usability of this MCG-based cardiac health monitoring smart clothing system has also been evaluated with technology acceptance model (TAM) analysis and the results indicate that the subject shows a positive attitude toward using this wearable MCG-based cardiac health monitoring and early warning system.

Realization of a CORDIC-Based Plug-In Accelerometer Module for PSG System in Head Position Monitoring for OSAS Patients

Overnight polysomnography (PSG) is currently the standard diagnostic procedure for obstructive sleep apnea (OSA). It has been known that monitoring of head position in sleep is crucial not only for the diagnosis (positional sleep apnea) but also for the management of OSA (positional therapy). However, there are no sensor systems available clinically to hook up with PSG for accurate head position monitoring. In this paper, an accelerometer-based sensing system for accurate head position monitoring is developed and realized. The core CORDIC- (COordinate Rotation DIgital Computer-) based tilting sensing algorithm is realized in the system to quickly and accurately convert accelerometer raw data into the desired head position tilting angles. The system can hook up with PSG devices for diagnosis to have head position information integrated with other PSG-monitored signals. It has been applied in an IRB test in Taipei Veterans General Hospital and has been proved that it can meet the medical needs of accurate head position monitoring for PSG diagnosis.