As the life expectancy of individuals increases with recent advancements in medicine and quality of living, it is important to monitor the health of patients and healthy individuals on a daily basis. This is not possible with the current health care system in North America, and thus there is a need for wireless devices that can be used from home. These devices are called biomedical wearables, and they have become popular in the last decade. There are several reasons for that, but the main ones are: expensive health care, longer wait times, and an increase in public awareness about improving quality of life. With this, it is vital for anyone working on wearables to have an overall understanding of how they function, how they were designed, their significance, and what factors were considered when the hardware was designed. Therefore, this study attempts to investigate the hardware components that are required to design wearable devices that are used in the emerging context of the Internet of Medical Things (IoMT). This means that they can be used, to an extent, for disease monitoring through biosignal capture. In particular, this review study covers the basic components that are required for the front-end of any biomedical wearable, and the limitations that these wearable devices have. Furthermore, there is a discussion of the opportunities that they create, and the direction that the wearable industry is heading in.

To assess the gait variability in patients with Parkinson's disease (PD), we first used the nonparametric Parzen-window method to estimate the probability density functions (PDFs) of stride interval and its two subphases (i.e., swing interval and stance interval). The gait rhythm standard deviation (¿) parameters computed with the PDFs indicated that the gait variability is significantly increased in PD. Signal turns count (STC) was also derived from each outlier-processed gait rhythm time series to serve as a dominant feature, which could be used to characterize the gait variability in PD. Since it was observed that the statistical parameters of swing interval or stance interval were highly correlated with those of stride interval, this article only used the stride interval parameters, i.e., ¿r and STCr , to form the feature vector in the pattern classification experiments. The results evaluated with the leave-one-out cross-validation method demonstrated that the least squares support vector machine with polynomial kernels was able to provide a classification accurate rate of 90.32% and an area (Az) of 0.952 under the receiver operating characteristic curve, both of which were better than the results obtained with the linear discriminant analysis (accuracy: 67.74%, Az: 0.917). The features and the classifiers used in the present study could be useful for monitoring of the gait in PD.

Vibroarthrographic (VAG) signals emitted by human knee joints are nonstationary and multicomponent in nature; time-frequency distributions (TFD's) provide powerful means to analyze such signals. The objective of this paper is to construct adaptive TFD's of VAG signals suitable for feature extraction. An adaptive TFD was constructed by minimum cross-entropy optimization of the TFD obtained by the matching pursuit decomposition algorithm. Parameters of VAG signals such as energy, energy spread. frequency, and frequency spread were extracted from their adaptive TFD's. The parameters carry information about the combined TF dynamics of the signals. The mean and standard deviation of the parameters were computed, and each VAG signal was represented by a set of just six features. Statistical pattern classification experiments based on logistic regression analysis of the parameters showed an overall normal/abnormal screening accuracy of 68.9% with 90 VAG signals (51 normals and 39 abnormals), and a higher accuracy of 77.5% with a database of 71 signals with 51 normals and 20 abnormals of a specific type of patellofemoral disorder. The proposed method of VAG signal analysis is independent of joint angle and clinical information, and shows good potential for noninvasive diagnosis and monitoring of patellofemoral disorders such as chondromalacia patella.