Ren-Guey Lee

Wireless Health Care Service System for Elderly With Dementia

The purpose of this paper is to integrate the technologies of radio frequency identification, global positioning system, global system for mobile communications, and geographic information system (GIS) to construct a stray prevention system for elderly persons suffering from dementia without interfering with their activities of daily livings. We also aim to improve the passive and manpowered way of searching the missing patient with the help of the information technology. Our system provides four monitoring schemes, including indoor residence monitoring, outdoor activity area monitoring, emergency rescue, and remote monitoring modes, and we have developed a service platform to implement these monitoring schemes. The platform consists of a web service server, a database server, a message controller server, and a health-GIS (H-GIS) server. Family members or volunteer workers can identify the real-time positions of missing elderly using mobile phone, PDA, Notebook PC, and various mobile devices through the service platform. System performance and reliability is analyzed. Experiments performed on four different time slots, from three locations, through three mobile telecommunication companies show that the overall transaction time is 34 s and the average deviation of the geographical location is about 8 m. A questionnaire surveyed by 11 users show that eight users are satisfied with the system stability and 10 users would like to carry the locating device themselves, or recommend it to their family members

A Healthcare Integration System for Disease Assessment and Safety Monitoring of Dementia Patients

Elderly dementia patients often get lost due to lack of a sense of direction. This may put them at risk and cause their families much worry. The goal of this study is to use information technology to enhance the professional judgment of caregivers, strengthen internal safety monitoring at care organizations, and improve the quality of care for dementia patients. An eXtensible-Markup-Language-based dementia assessment system combining program code and assessment content is used to provide caregivers with better flexibility and real-time response ability. Beyond establishing long-term case files, the system can also perform data consistency analysis, strengthen caregiverspsila continuing education, improve caregiverspsila case judgment skills, and reduce the incidence of accidents due to neglect. This study also applies radio frequency identification (RFID) technology to the development of an indoor and outdoor active safety monitoring mechanism. The system can automatically remind caregivers whenever an elderly person approaches a dangerous area or strays too far. Apart from the use of different size tags, the realization of the system also employs the tame transformation signatures (TTS) algorithm to encrypt tag IDs and protect personal privacy. Clinical testing of the system showed that the indoor RFID reader has a response time of 0.5 s when sensing 40 tags, while the outdoor reader has a sensing time of approximately 5 s due to the need to save power. In the latter case, the system can ensure that elderly patients stay less than 15 m away from their caregivers. Patients were relatively willing to wear light tags. We also found that irritable patients with strong mobility were less compliant and often removed their own tags. Caregivers must provide active care and adopt various safety measures to protect the type of patients.

Design and implementation of a mobile-care system over wireless sensor network for home healthcare applications.

According to home healthcare requirement of chronic patients, this paper proposes a mobile-care system integrated with a variety of vital-sign monitoring, where all the front-end vital-sign measuring devices are portable and have the ability of short-range wireless communication. In order to make the system more suitable for home applications, the technology of wireless sensor network is introduced to transmit the captured vital signs to the residential gateway by means of multi-hop relay. Then the residential gateway uploads data to the care server via Internet to carry out patients condition monitoring and the management of pathological data. Furthermore, the system is added in the alarm mechanism, which the portable care device is able to immediately perceive the critical condition of the patient and to send a warning message to medical and nursing personnels in order to achieve the goal of prompt rescue

A NEW APPROACH FOR IDENTIFYING SLEEP APNEA SYNDROME USING WAVELET TRANSFORM AND NEURAL NETWORKS

This paper describes a new technique to classify and analyze the electroencephalogram (EEG) signal and recognize the EEG signal characteristics of Sleep Apnea Syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN). The EEG signals are separated into Delta, Theta, Alpha, and Beta spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. We treated the wavelet coefficient as the kind of the training input of artificial neural network, might result in 6 groups of wavelet coefficients per second signal by way of characteristic part processing technique of the artificial neural network designed by our group, we carried out the task of training and recognition of SAS symptoms. Then the neural network was configured to give three outputs to signify the SAS situation of the patient. The recognition threshold for all test signals turned out to have a sensitivity level of approximately 69.64% and a specificity value of approximately 44.44%. In neurology clinics, this study offers a clinical reference value for identifying SAS, and could reduce diagnosis time and improve medical service efficiency.

A NOVEL QRS DETECTION ALGORITHM APPLIED TO THE ANALYSIS FOR HEART RATE VARIABILITY OF PATIENTS WITH SLEEP APNEA

Sleep-related breathing disorders can cause heart rate changes known as cyclical variation. The heart rate variation of patients with obstructive sleep apnea syndrome (OSAS) is more prominent in sleep. For this reason, to analyze heart rate variability (HRV) of patients with sleep apnea is a very important issue that can assist physicians to diagnose and give suitable treatment for patients. In this paper, a novel QRS detection algorithm is developed and applied to the analysis for HRV of patients with sleep apnea. The advantageous of the proposed algorithm is the combination of digital filtering and reverse R wave detection techniques to enhance the accuracy of R wave detection and easily implement into portable ECG monitoring system with light complexities of computation. The proposed algorithm is verified by simulation and experimental results.

Design and implementation of wireless multi-channel EEG recording system and study of EEG clustering method

A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patients EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. First, the NLEO algorithm is utilized to divide the EEG signals into many small signal segments according to the features of the amplitude and frequency of EEG signals. The AR model is then applied to extract two characteristic values, i.e., frequency and amplitude (peak to peak value), of each segment and to form characteristic matrix for each segment of EEG signal. Finally, the improved modified k-means algorithm is utilized to assort similar EEG segments into better data classification, which allows accessing the long-term EEG signals more quickly.

Comparison of similarity measures for clustering electrocardiogram complexes

This paper compares four similarity measures such as the city block (L1-norm), the Euclidean (L2-norm), the normalized correlation coefficient, and the simplified gray relational grade for clustering QRS complexes. Performances of the measures include classification accuracy, threshold value selection, noise robustness, and execution time. The clustering algorithm used is the so-called two-step unsupervised method. The best out of the 10 independent runs of the clustering algorithm with randomly selected initial template beat for each run is used to compare the performances of each similarity measure. Simulation results show that the simplified gray relational grade outperforms the other measures

A MOBILE-CARE SYSTEM OVER WIRELESS SENSOR NETWORK FOR HOME HEALTHCARE APPLICATIONS

In response to the home healthcare requirement of chronic patients, this paper proposes a mobile-care system integrated with a variety of vital-sign monitorings, where all the front-end vital-sign measuring devices are portable and have the ability of short-range wireless communication. In order to make the system suitable for home applications, wireless sensor network is introduced to transmit the captured vital signs to the residential gateway by means of multi-hop relay. Then the residential gateway uploads data to the care server via internet to carry out patients condition monitoring and the management of pathological data. Furthermore, the system is equipped with the alarm mechanism, where the portable care device is able to immediately perceive the critical condition of the patient and send a warning message to medical and nursing personnels to achieve the goal of prompt rescue.

Active RFID System with Cryptography and Authentication Mechanisms

Radio frequency identification (RFID) systems have recently been used in a large number of applications. Security and privacy issues have also imposed significant chal- lenges on these systems. Cryptography and authentication protocols have been utilized to effectively solve the security and privacy problems in RFID systems. In this paper, we integrate public key encryption, embedded computation, and wire- less communication technologies into the active RFID system that we proposed with cryptography and authentication mechanisms. In our proposed active RFID system, a se- cure RFID Tag intermittently transmits cipher text to a RFID Reader which then trans- mits in multi-hop relaying to a back-end platform to perform data comparison for authen- tication. In addition, the digital signature scheme - Tame Transformation Signatures (TTS) has the advantages of high security, high-speed key generation, signature, and suitability to embedded systems and is thus suitable to be used in our authentication sys- tem. It is used in our proposed system to protect the plain text. The TTS algorithm is from the family of asymmetric public key systems and thus has superiority such as better security, fast key generation, complex algorithm, and low signature delay. The TTS algo- rithm can thus encrypt data and perform authentication more effectively in active RFID systems. We have three major contributions in this paper. The first is to fully design and im- plement an active RFID system which includes active Tags and Readers. In our system, the Tag can stand by and keep working in long term after getting started. The second is to successfully implement a proposed active RFID system with TTS cryptography and au- thentication mechanisms to protect the content in tags to ensure the security in multi-hop transmission. The last is to adopt multi-hop relays to extend distance between Readers.

A comparison of similarity measures for clustering of QRS complexes

Similarity or distance measures play important role in the performance of algorithms for ECG clustering problems. This paper compares four similarity measures such as the city block (L1-norm), Euclidean (L2-norm), normalized correlation coefficient, and simplified grey relational grade for clustering of QRS complexes. Performances of the measures include classification accuracy, threshold value selection, noise robustness, execution time, and the capability of automated selection of templates. The clustering algorithm used is the so-called two-step unsupervised method. The best out of the 10 independent runs of the clustering algorithm with randomly selected initial template beat for each run is used to compare the performances of each similarity measure. To investigate the capability of automated selection of templates for ECG classification algorithms, we use the cluster centers generated by the clustering algorithm with various measures as templates. Four sets of templates are obtained, each set for a measure. And the four sets of templates are used in the k-nearest neighbor classification method to evaluate the performance of the templates. Tested with MIT/BIH arrhythmia data, we observe that the simplified grey relational grade outperforms the other measures in classification accuracy, threshold value selection, noise robustness, and the capability of automated selection of templates.