Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.

/pdf/gait-analysis-using-wearable-sensors-x1b4m112uw.pdf

Gait analysis using wearable sensors.

Photoplethysmography (PPG) technology has been used to develop small, wearable, pulse rate sensors. These devices, consisting of infrared light-emitting diodes (LEDs) and photodetectors, offer a simple, reliable, low-cost means of monitoring the pulse rate noninvasively. Recent advances in optical technology have facilitated the use of high-intensity green LEDs for PPG, increasing the adoption of this measurement technique. In this review, we briefly present the history of PPG and recent developments in wearable pulse rate sensors with green LEDs. The application of wearable pulse rate monitors is discussed.

Wearable Photoplethysmographic Sensors—Past and Present

Heart rate monitoring using wrist-type photoplethysmographic signals during subjects’ intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects’ hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.

TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal's second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.

/pdf/a-review-on-wearable-photoplethysmography-sensors-and-their-3kmqsjhauj.pdf

A review on wearable photoplethysmography sensors and their potential future applications in health care.

Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a Gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than −16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal.

https://iopscience.iop.org/article/10.1088/0967-3334/33/2/259/pdf

Wavelet-based motion artifact removal for functional near-infrared spectroscopy.

A photoplethysmography (PPG) signal provides very useful information about a subject's hemodynamic status in a hospital or ubiquitous environment. However, PPG is very vulnerable to motion artifacts, which can significantly distort the information belonging to the PPG signal itself. Thus, the reduction of the effects of motion artifacts is an important issue when monitoring the cardiovascular system by PPG. There have been many adaptive techniques to reduce motion artifacts from PPG signals. In the present study, we compared a method based on the fixed-interval Kalman smoother with the usual adaptive filtering algorithms, e.g. the normalized least mean squares, recursive least squares and the conventional Kalman filter. We found that the fixed-interval Kalman smoother reduced motion artifacts from the PPG signal most effectively. Therefore, the use of the fixed-interval Kalman smoother can reduce motion artifacts in PPG, thus providing the most reliable information that can be deduced from the reconstructed PPG signals.

/pdf/improved-elimination-of-motion-artifacts-from-a-1l1h4dzebx.pdf

Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry

Detection of apneic events from single channel nasal airflow using 2nd derivative method

Due to the aging process, the aftereffect of an accident or the problems in brain, some people can experience difficulties in walking. It could be helpful in the treatment of these individuals suffering walking difficulties to monitor their movement. The unconstrained activity monitoring system (U-AMS) was developed to monitor the body's acceleration. It can be attached easily and doesn't interfere with daily activity. The hardware consists of 3-axis accelerometer, which records data for long periods of time. The movement image was simultaneously recorded by a camcorder. To collect data from U-AMS and analyze them, several programs were developed. The data were transferred to a computer by RS-232 module and displayed through viewing program made by MFC (Microsoft, USA). The analyzing program, which links viewing program to MATLAB (Mathworks, USA), analyzes the data to detect movement disorder. In this paper, freezing, which is characterized as start hesitation., was the target disorder. Using the different characteristics in frequency domain between freezing and normal gait, freezing appearances were detected. By comparing with movement image, the accuracy of the freezing detection was verified to have a good performance.

Gait analysis for freezing detection in patients with movement disorder using three dimensional acceleration system

The present invention relates to an apparatus for analyzing a sleep structure according to non-restrictive weight detection. The apparatus includes load cells one installed in the predetermined locations of a bed and configured to detect the weight of a target person; low-pass filters for outputting low-pass filtered signals that belong to the outputs of the load cells; high-pass filters for outputting high-pass filtered signals that belong to the outputs of the load cells; a multiplexer for selecting any one from among the outputs of the low-pass filters and the high-pass filters; a controller configured to detect information about the target person's sleep from the low-pass filtered signals and the high-pass filtered signals, which belong to the outputs of the high-pass filters and the low-pass filters, respectively, and are input through the multiplexer, and determine the target person's sleeping pattern; and a display unit for displaying determination results of the controller.

Apparatus for analyzing a sleep structure according to non-constrained weight detection

The purpose of this paper is the classification of Spatial-Temporal Image of Plantar pressure (STIP) among normal step and the patients step of Parkinson disease. For this, we created a new image data, STIP, that have information of the change of plantar pressure during heel to toe motion (i.e., contain spatial and temporal information for plantar pressure). To get STIP, the walking of 21 patients with Parkinson disease and 17 age-matched healthy subjects were recorded and analyzed using in-shoe dynamic pressure measuring system with comfort walking. For feature extraction of gait, we applied Principal component analysis (PCA) to STIP and calculated weights of STIP on each principal components. Then, we build hard margin Support Vector Machine (SVM) classifier for gait recognition and test of generalization performance using normalized weights on PCs of STIP. SVM result indicated an overall accuracy of 91.73% by the RBF(Radial Basis Function) kernel function. These results demonstrate considerable potential in applying SVMs in gait classification for many applications.

Jonghee Han

Papers

Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry

Detection of apneic events from single channel nasal airflow using 2nd derivative method

Gait analysis for freezing detection in patients with movement disorder using three dimensional acceleration system

Apparatus for analyzing a sleep structure according to non-constrained weight detection

Classification of Parkinson gait and normal gait using Spatial-Temporal Image of Plantar pressure