Automatically detecting asymmetric running using time and frequency domain features

Human motion analysis technologies have been widely employed to identify injury determining factors and provide objective and quantitative feedback to athletes to help prevent injury. However, most of these technologies are: ex- pensive, restricted to laboratory environments, and can require significant post processing. This reduces their ecological validity, adoption and usefulness. In this paper, we present a novel wearable inertial sensor framework to accurately distinguish between symmetrical and asymmetrical running patterns in an unconstrained environment. The framework can automatically classify symmetry/asymmetry using Short Time Fourier Trans- form (STFT) and other time domain features in conjunction with a customized Random Forest classifier. The accuracy of the designed framework is up to 94% using 3-D accelerometer and 3-D gyroscope data from a sensor node attached on the upper back of a subject. The upper back inertial sensors data were then down-sampled by a factor of 4 to simulate utilizing low-cost inertial sensors whilst also facilitating a decrease of the computational cost to achieve near real-time application. We conclude that the proposed framework can potentially pave the way for employing low-cost sensors, such as those used in smartphones, attached on the upper back to provide injury related and performance feedback in real-time in unconstrained environments

What Shall We Teach our Pants?

If a wearable device can register what the wearer is currently doing, it can anticipate and adjust its behavior to avoid redundant interaction with the user. However, the relevance and properties of the activities that should be recognized depend on both the application and the user. This requires an adaptive recognition of the activities where the user, instead of the designer, can teach the device what he/she is doing. As a case study, we connected a pair of pants with accelerometers to a laptop to interpret the raw sensor data. Using a combination of machine learning techniques such as Kohonen maps and probabilistic models, we build a system that is able to learn activities while requiring minimal user attention

An inertial measurement-based gait detection system for active leg prostheses

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (leaves 45-46).Active leg prostheses can lead to more natural and less energy consuming gait patterns for amputees than passive prostheses can, because they provide a better approximation of the functions of the human leg. Active prostheses use motors to supply torques for added force and greater control at the joints (replacing the functions of normal limb musculature). The necessary amount of torque to apply must be closely correlated with gait characteristics. To properly control an active prosthesis, it is necessary to determine whether one is walking at a stable or varying velocity, on level ground, stairs, or a hill or ramp, and in the latter cases whether one is ascending or descending. In all cases, it is essential to detect transitions between gaits as early as possible, ideally before the foot makes contact with the ground, in order for the control system to adjust accordingly. In this thesis, a sensor system for a lower leg prosthesis is described, and a method for determining the gait transitions from this system are presented. The sensor system consists of an inertial measurement unit comprising three accelerometers and three rate gyroscopes installed on the prosthetic limb and a set of strain gauges on the limb to detect changes in force. Using this instrumented prosthesis, data are collected while an amputee participant transitions from level ground to stair ascent/descent. These data are then processed using an intent recognition method based on a hybrid discrete-continuous physical model of human walking. This method is evaluated for accuracy and robustness for real-time use.by Benjamin Baruch Aisen.S.M

Accelerometry-Based Classification of Human Activities Using Markov Modeling

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

Combining low cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. We present a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system - Smartphone Brain Scanner - combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully mobile system for real-time 3D EEG imaging. We discuss the benefits and challenges of a fully portable system, including technical limitations as well as real-time reconstruction of 3D images of brain activity. We present examples of the brain activity captured in a simple experiment involving imagined finger tapping, showing that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using a off-the-shelf consumer neuroheadset is lower compared to that obtained using high density standard EEG equipment, we propose that mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings

Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series