Low-cost Wearable Sensors Research Articles

Pollution sensor keeps you out of choking air

On Tuesday, the world's first low-cost wearable air quality sensor went on sale, capable of monitoring your exposure to the three most harmful pollutants. The gadget, called Flow, can be used as a handheld sensor or attached to pushchairs, purses and bags, and will set you back about $200.

PhysioLab - a multivariate physiological computing toolbox for ECG, EMG and EDA signals: a case of study of cardiorespiratory fitness assessment in the elderly population

The exponential increase of wearable health-tracking technologies offers new possibilities but also poses new challenges in signal processing to enable fitness monitoring through multimodal physiological recordings. Although there are several software tools used for post-processing in physiological computing applications, limitations in the analysis, incorporating signals from multiple sources, integrating contextual information and providing information visualization tools prevent a widespread use of this technology. To address these issues, we introduce PhysioLab, a multimodal processing Matlab tool for the data analysis of Electromyography (EMG), Electrocardiography (ECG) and Electrodermal Activity (EDA). The software is intended to facilitate the processing and comprehension of multimodal physiological data with the aim of assessing fitness in several domains. A unique feature of PhysioLab is that is informed by normative data grouped by age and sex, allowing contextualization of data based on users’ demographics. Besides signal processing, PhysioLab includes a novel approach to multivariable data visualization with the aim of simplifying interpretation by non-experts users. The system computes a set of ECG features based on heart rate variability analysis, EMG parameters to quantify force and fatigue levels, and galvanic skin level/responses from EDA signals. Furthermore, PhysioLab provides compatibility with data from multiple low-cost wearable sensors. We conducted an experiment with 17 community-dwelling older adults (64.5 ± 6.4) to assess the feasibility of the tool in characterizing cardiorespiratory profiles during physical activity. Correlation analyses and regression models showed significant interactions between physiology and fitness evaluations. Our results suggest novel ways that physiological parameters could be effectively used to complement traditional fitness assessment.

Cardiorespiratory Model-Based Data-Driven Approach for Sleep Apnea Detection.

Obstructive sleep apnea (OSA) is a chronic sleep disorder affecting millions of people worldwide. Individuals with OSA are rarely aware of the condition and are often left untreated, which can lead to some serious health problems. Nowadays, several low-cost wearable health sensors are available that can be used to conveniently and noninvasively collect a wide range of physiological signals. In this paper, we propose a new framework for OSA detection in which we combine the wearable sensor measurement signals with the mathematical models of the cardiorespiratory system. Vector-valued Gaussian processes (GPs) are adopted to model the physiological variations among different individuals. The GP covariance is constructed using the sum of separable kernel functions, and the GP hyperparameters are estimated by maximizing the marginal likelihood function. A likelihood ratio test is proposed to detect OSA using the widely available heart rate and peripheral oxygen saturation (SpO ) measurement signals. We conduct experiments on both synthetic and real data to show the effectiveness of the proposed OSA detection framework compared to purely data-driven approaches.

Hankelet-based action classification for motor intention recognition

Powered lower-limb prostheses require a natural, and an easy-to-use, interface for communicating amputee’s motor intention in order to select the appropriate motor program in any given context, or simply to commute from active (powered) to passive mode of functioning. To be widely accepted, such an interface should not put additional cognitive load at the end-user, it should be reliable and minimally invasive.In this paper we present a one such interface based on a robust method for detecting and recognizing motor actions from a low-cost wearable sensor network mounted on a sound leg providing inertial (accelerometer, gyrometer and magnetometer) data in real-time. We assume that the sensor measurement trajectories – in a given temporal window – can be represented as the output of a linear time invariant system. We describe such set of trajectories via a Hankel matrix, which embeds the observability matrix of the LTI system generating the set of trajectories. The use of Hankel matrices (known as Hankelets) avoids the burden of performing system identification while providing a computationally convenient descriptor for the dynamics of a time-series.For the recognition of actions, we use two off-the-shelf classifiers, nearest neighbor (NN) and support vector machines (SVM), in cross-subject validation. We present results using either the joint angles or the raw sensor data showing a net improvement of the Hankelet-based approach against a baseline method. In addition, we compare results on action recognition using joint angles provided by trakSTAR, a high-accuracy motion tracking unit, demonstrating – somewhat surprisingly – that best results (in terms of average recognition accuracy over different actions) are provided by raw inertial data, paving the way towards a wider usage of our method in the field of active prosthetics, in particular, and motor intention recognition, in general.

Low Cost Wearable Sensor for Human Emotion Recognition Using Skin Conductance Response

Malaysia is one of the countries with the highest car crash fatality rates in Asia. The high implementation cost of in-vehicle driver behavior warning system and autonomous driving remains a significant challenge. Motivated by the large number of simple yet effective inventions that benefitted many developing countries, this study presents the findings of emotion recognition based on skin conductance response using a low-cost wearable sensor. Emotions were evoked by presenting the proposed display stimulus and driving stimulator. Meaningful power spectral density was extracted from the filtered signal. Experimental protocols and frameworks were established to reduce the complexity of the emotion elicitation process. The proof of concept in this work demonstrated the high accuracy of two-class and multiclass emotion classification results. Significant differences of features were identified using statistical analysis. This work is one of the most easy-to-use protocols and frameworks, but has high potential to be used as biomarker in intelligent automobile, which helps prevent accidents and saves lives through its simplicity.

Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor.

Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.

Welcome to MTI—A New Open Access Journal Dealing with Blue Sky Research and Future Trends in Multimodal Technologies and Interaction

In this era of massive use of computers and other computational devices (e.g., low-cost wearable sensors, smartphones, other smart devices, etc.), the nature of digital data is becoming more complex and heterogeneous.

Experimental Validation of the Tyndall Portable Lower-limb Analysis System with Wearable Inertial Sensors

Biomechanical analysis of movement during sport practice is extremely useful to assess and, subsequently, optimise movement performance during sport which can also assist athletes during rehabilitation following injury (such as Anterior Cruciate Ligament reconstruction). It is mostly performed using camera-based motion analysis systems, which provide good results but present serious drawbacks (for instance, consistent size, high cost, and lack of portability). Thus, small-size low-cost wearable sensors are an emerging tool for biomechanics monitoring. Aim of the present work is to implement a novel wireless portable easy-to-use system, consisting of two Tyndall Wireless Inertial Measurement Units (WIMUs) per leg, suitable for free-living environments and able to provide a complete biomechanics assessment (generated on a report) without the constraints of a laboratory. Validation for the lower-limbs using state-of-the-art camera-based motion capture is presented here. Algorithms are implemented in Matlab, and the scenarios considered simulate a free-living environment and exercises performed in a rehabilitation procedure. The system has been validated with healthy and impaired subjects. This novel system shows high accuracy values for all considered scenarios. Moreover, it is able to detect atypical movement characteristics. The results of this feasibility study support the next phase which will be to assess the external and ecological validity of athletes’ on-field movement performance, which will help to inform individualised training protocols or enhance targeted rehabilitation programmes.

Human Joint Angle Estimation with Inertial Sensors and Validation with A Robot Arm.

Traditionally, human movement has been captured primarily by motion capture systems. These systems are costly, require fixed cameras in a controlled environment, and suffer from occlusion. Recently, the availability of low-cost wearable inertial sensors containing accelerometers, gyroscopes, and magnetometers have provided an alternative means to overcome the limitations of motion capture systems. Wearable inertial sensors can be used anywhere, cannot be occluded, and are low cost. Several groups have described algorithms for tracking human joint angles. We previously described a novel approach based on a kinematic arm model and the Unscented Kalman Filter (UKF). Our proposed method used a minimal sensor configuration with one sensor on each segment. This paper reports significant improvements in both the algorithm and the assessment. The new model incorporates gyroscope and accelerometer random drift models, imposes physical constraints on the range of motion for each joint, and uses zero-velocity updates to mitigate the effect of sensor drift. A high-precision industrial robot arm precisely quantifies the performance of the tracker during slow, normal, and fast movements over continuous 15-min recording durations. The agreement between the estimated angles from our algorithm and the high-precision robot arm reference was excellent. On average, the tracker attained an RMS angle error of about 3(°) for all six angles. The UKF performed slightly better than the more common Extended Kalman Filter.

BioKin: an ambulatory platform for gait kinematic and feature assessment

A platform to move gait analysis, which is normally restricted to a clinical environment in a well-equipped gait laboratory, into an ambulatory system, potentially in non-clinical settings is introduced. This novel system can provide functional measurements to guide therapeutic interventions for people requiring rehabilitation with limited access to such gait laboratories. BioKin system consists of three layers: a low-cost wearable wireless motion capture sensor, data collection and storage engine, and the motion analysis and visualisation platform. Moreover, a novel limb orientation estimation algorithm is implemented in the motion analysis platform. The performance of the orientation estimation algorithm is validated against the orientation results from a commercial optical motion analysis system and an instrumented treadmill. The study results demonstrate a root-mean-square error less than 4° and a correlation coefficient more than 0.95 when compared with the industry standard system. These results indicate that the proposed motion analysis platform is a potential addition to existing gait laboratories in order to facilitate gait analysis in remote locations.

Posture estimation and human support using wearable sensors and walking-aid robot

In this paper, an omni-directional walking-aid robot is developed to assist the elderly in the daily living movements. A motion control strategy of walking-aid robot based on the observation of the human status through wearable sensors is proposed. During normal walking, the robot is controlled using a conventional admittance control scheme. When the tendency of a fall is detected, the robot will immediately react to prevent the user from falling down. The distance between the human Center of Pressure (COP) and the midpoint of the human feet is assumed to be a significant feature to detecting the fall events. When the user is in a quasi-static state or walking slowly, the COP can be approximated by the projection of Center of Gravity (COG) of the user’s body. A simple and low-cost wearable sensor system is proposed to measure online the COG of the user. A limitation of the proposed wearable sensor system is that the Head–Arms–Torso (HAT) of the user is assumed to be always in upright position, which may generate measurement error. From comparison experiments with a reference optical system it is found that the measurement error is acceptable especially at the early stage of fall event. Dubois possibility theory is applied to describe the membership function of “normal walking” state. A threshold based fall detection approach is obtained from online evaluation of the walking status. Finally, experiments demonstrate the validity of the proposed strategy.

Smart applications to track and record physical activity: implications for obesity treatment

Smart applications to track and record physical activity: implications for obesity treatment Siew Sun Wong,1 Yu Meng,1 Paul D Loprinzi,2 Nobuko Hongu3 1School of Biological and Population Health Sciences, Oregon State University, Corvallis, OR, USA; 2Department of Exercise Science, Donna and Allan Lansing School of Nursing and Health Sciences, Bellarmine University, Louisville, KY, USA; 3Department of Nutritional Sciences, University of Arizona, Tucson, AZ, USA Abstract: The primary purpose of this review is to answer three research questions: 1) What are the most popular features of physical activity (PA) tracking and recording mobile applications (apps); 2) what features drive the app cost, peak rank, or gross sales; and 3) to what extent are evidence-based weight loss recommendations used in PA apps? Two hundred top grossing iOS health care and fitness apps were screened using a systematic review method. Fifty-five apps met the criteria as PA tracking and recording apps. Nearly half of these iOS PA apps are also available in Android. Two separate reviewers evaluated each PA app using 48 features. The top three most popular features are the use of behavioral strategies, use of the FITT Principles (frequency, intensity, time, type), and the use of the Principles for Physical Fitness. Free apps contain a mean of nine of the ten most popular features in paid apps, making them good bargains in PA promotion. Better peak rank is associated with the use of Fitness Principles, weight loss goal setting, or training videos. Ten PA apps met at least four of eight of the American College of Sports Medicine (ACSM) recommendations for weight loss and prevention of weight regain for adults. The least popular features were PA safety and workouts for special need populations. Implications for obesity treatment are discussed in relationship to individual end-users, health care providers, and app developers. Because current PA apps still lack validity and compliance to standards for PA safety and data security, medical consultation for weight loss is recommended. The quality of PA apps on the market will improve through innovative support from the government, industry, and academia. The future of PA apps is bright, especially with the potential of using emerging technologies that involve low-cost wearable sensors, global information systems, and immersive technology such as virtual worlds and virtual reality. Keywords: Android, exercise, iOS, mHealth, self-monitoring, technology

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

The wide availability of low-cost wearable biophysiological sensors enables us to measure how the environment and our experiences impact our physiology. This creates a challenge: in order to interpret the longitudinal data, we require the matching contextual information as well. Collecting continuous biophysiological data makes it unfeasible to rely solely on our memory for contextual information. In this paper, we first present an architecture and implementation of a system for the acquisition, processing, and visualization of biophysiological signals and contextual information. Next, we present the results of a user study: users wore electrodermal activity wrist sensors that measured their autonomic arousal. These users uploaded the sensor data at the end of each day. At first, they annotated their events at the end of each day; then, after a two-day break, they annotated the data from two days earlier. One group of users had access to both the signal and the contextual information collected by the mobile phone and the other group could only access the biophysiological signal. At the end of the study, the users filled in a system usability scale and user experience surveys. Our results show that the system enables the users to annotate biophysiological signals at a greater effectiveness than the current state of the art while also providing very good usability.

Conversation Monitoring via Low-cost Speaker Diarization using Wearable Wireless Sensors

This paper presents speaker diarization mechanisms using low-cost and low-resolution wearable wireless sensors. Speaker diarization is used for identifying speaking sequence and duration for all individuals engaged in a conversation session. The key advantage of the proposed mechanisms is their ability to monitor human conversation without having to perform energy- and processing-expensive speaker identification algorithms. A prototype system was constructed for experimental acoustic diarization using low-cost and low-resolution wearable sensors. It was experimentally demonstrated that an inexpensive threshold-based diarization mechanism can be used for conversation monitoring with acceptable accuracy. But for more detection accuracy, an acoustic comparator-based diarization is applied. It was shown that comparator-based diarization mechanism is able to consistently deliver significantly better acoustic detection performance than threshold-based mechanism in a more distance and noise independent manner. Controlled experiments using human subjects were carried out for evaluating diarization accuracy and the sensitivity to factors such as sampling rate and inter-speaker distance during conversations.