Haixia Yang¹, Yixian Shen², Wei Zhuang², Chunming Gao³, Dong Dai⁴, Weigong Zhang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1217-1238, 2021, DOI:10.32604/cmes.2021.014558

Abstract Parkinson’s disease (PD) is a very common neurodegenerative disease that occurs mostly in the elderly. There are many main clinical manifestations of PD, such as tremor, bradykinesia, muscle rigidity, etc. Based on the current research on PD, the accurate and convenient detection of early symptoms is the key to detect PD. With the development of microelectronic and sensor technology, it is much easier to measure the barely noticeable tremor in just one hand for the early detection of Parkinson’s disease. In this paper, we present a smart wearable device for detecting hand tremor, in which MPU6050 (MIDI Processing Unit) consisting… More >

Vankamamidi S. Naresh^1,∗,†, Suryateja S. Pericherla^2,‡, Pilla Sita Rama Murty^3,§, Sivaranjani Reddi^4,¶

Computer Systems Science and Engineering, Vol.35, No.6, pp. 411-421, 2020, DOI:10.32604/csse.2020.35.411

Abstract Healthcare, the largest global industry, is undergoing significant transformations with the genesis of a new technology known as the Internet of Things (IoT). Many healthcare leaders are investing more money for transforming their services to harness the benefits provided by IoT, thereby paving the way for the Internet of Medical Things (IoMT), an extensive collection of medical sensors and associated infrastructure. IoMT has many benefits like providing remote healthcare by monitoring health vitals of patients at a distant place, providing healthcare services to elderly people, and monitoring a large group of people in a region or country for detection and… More >

Sun-Taag Choe, We-Duke Cho^*, Jai-Hoon Kim, and Ki-Hyung Kim

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 679-691, 2020, DOI:10.32604/iasc.2020.010102

Abstract Recent research on activity recognition in wearable devices has identified a key challenge: k-nearest neighbors (k-NN) algorithms have a high operational time complexity. Thus, these algorithms are difficult to utilize in embedded wearable devices. Herein, we propose a method for reducing this complexity. We apply a clustering algorithm for learning data and assign labels to each cluster according to the maximum likelihood. Experimental results show that the proposed method achieves effective operational levels for implementation in embedded devices; however, the accuracy is slightly lower than that of a traditional k-NN algorithm. Additionally, our method provides the advantage of controlling the… More >

Qiaoyang Zhang¹, Zhiyao Liang^1,*, Zhiping Cai²

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 457-471, 2019, DOI:10.32604/cmc.2019.03758

Abstract Wearable devices are becoming more popular in our daily life. They are usually used to monitor health status, track fitness data, or even do medical tests, etc. Since the wearable devices can obtain a lot of personal data, their security issues are very important. Motivated by the consideration that the current pairing mechanisms of Bluetooth Low Energy (BLE) are commonly impractical or insecure for many BLE based wearable devices nowadays, we design and implement a security framework in order to protect the communication between these devices. The security framework is a supplement to the Bluetooth pairing mechanisms and is compatible… More >