Gaurav N. Pradhan

Body sensor networks to evaluate standing balance: interpreting muscular activities based on inertial sensors

In this paper, we present a system that integrates inertial sensors and electromyogram (EMG) signals, which measures the muscular activities while performing motions. The objective of our study is to investigate the behaviour of the EMG signals to interpret the activity of standing balance. Quantitative parameters for balance are obtained from an inertial sensor through a body-sensor network. These parameters are further used to find the prominent features in the EMG signal. The inertial sensor used in this system is an accelerometer. The implementation details and effectiveness of using EMG signals are also provided.

Indexing of variable length multi-attribute motion data

Haptic data such as 3D motion capture data and sign language animation data are new forms of multimedia data. The motion data is multi-attribute, and indexing of multi-attribute data is important for quickly pruning the majority of irrelevant motions in order to have real-time animation applications. Indexing of multi-attribute data has been attempted for data of a few attributes by using R-tree or its variants after dimensionality reduction. In this paper, we exploit the singular value decomposition (SVD) properties of multi-attribute motion data matrices to obtain one representative vector for each of the motion data matrices of dozens or hundreds of attributes. Based on this representative vector, we propose a simple and efficient interval-tree based index structure for indexing motion data with large amount of attributes. At each tree level, only one component of the query vector needs to be checked during searching, comparing to all the components of the query vector that should get involved if an R-tree or its variants are used for indexing. Searching time is independent of the number of pattern motions indexed by the tree, making the index structure well scalable to large data repositories. Experiments show that up to 91∼93% irrelevant motions can be pruned for a query with no false dismissals, and the query searching time is less than 30 μ <i>s</i> with the existence of motion variations.

Indexing 3-D Human Motion Repositories for Content-Based Retrieval

Content-based retrieval of the similar motions for the human joints has significant impact in the fields of physical medicine, biomedicine, rehabilitation, and motion therapy. In this paper, we propose an efficient indexing approach for 3-D human motion capture data, supporting queries involving both subbody motions as well as whole-body motions.

Hierarchical indexing structure for 3d human motions

Content-based retrieval of 3D human motion capture data has significant impact in different fields such as physical medicine, rehabilitation, and animation. This paper develops an efficient indexing approach for 3D motion capture data, supporting queries involving both sub-body motions (e.g., Find similar knee motions) as well as whole-body motions. The proposed indexing structure is based on the hierarchical structure of the human body segments consisting of independent index trees corresponding to each sub-part of the body. Each level of every index tree is associated with the weighted feature vectors of a body segment and supports queries on sub-body motions and also on whole-body motions. Experiments show that up to 97% irrelevant motions can be pruned for any kind of motion query while retrieving all similar motions, and one traversal of the index structure through all index trees takes on an average 15 μsec with the existence of motion variations.

Association rule mining in multiple, multidimensional time series medical data

Time series pattern mining (TSPM) finds correlations or dependencies in same series or in multiple time series. When the numerous instances of multiple time series data are associated with different quantitative attributes, they form a multiple multi-dimensional framework. In this paper, we consider real-life time series data of muscular activities of human participants obtained from multiple Electromyogram (EMG) sensors and discover patterns in these EMG data streams.

Hand-Gesture Computing for the Hearing and Speech Impaired

An instrumented data glove with a wireless interface provides convenient and natural human- computer interaction for people with speech or hearing impairments.

Integration of Motion Capture and EMG data for Classifying the Human Motions

Three dimensional motion capture facility is a powerful tool for quantitative and qualitative assessment of multi-joint external movements. Electro-myograph (EMG) signals give the physiologic information of muscles while doing motions. In this paper, our objective is to integrate these two different bio-medical data together and to extract precise and accurate feature information for classifying the human motions. When both forms of data are integrated and analyzed together; the information achieved will be immensely useful to quantify the complex human motions for medical reasons or sport performances. These biological quantifications of biomechanical data, are useful for gait analysis and several orthopedic applications, such as joint mechanics, prosthetic designs, and sports medicines. Vie different dimensionality reduction approaches such Integral of Absolute value and Weighted Singular Value Decomposition are used to extract the preliminary features from EMG and motion capture data respectively. On combining these feature vectors, fuzzy clustering such as Fuzzy c-means (FCM) is performed on these vectors that are mapped as the points in multi-dimensional feature space. We get the degree of memberships with every cluster for each mapped point. This extracted information is used as the final feature vectors for classifying the human motions.

Towards fatigue and intensity measurement framework during continuous repetitive activities

With the recent advancement in the wearable sensor technology there has been many studies about recognizing users activities, location or environment, but they did not recognize the effect of these activities on the physiological state of the person. The two major physiological aspects associated with any activity are intensity of activity and associated fatigue. Fatigue is an universal human experience that can negatively affect daily life activities. In this paper, we present a framework to measure the level of fatigue and intensity of activity during repetitive daily life activities. The proposed framework acquires and processes time series data from a surface Electromyogram (sEMG) sensor and employs state of art machine learning and data mining techniques to measure the physiological status. We tested this framework using the raw sEMG signals from the hand muscles of 10 subjects, including male and female, of age group around 25 to 45 years, collected during the continuous monitoring of repetitive palm movements at different repetition speeds. The framework graded the levels of fatigue and intensity of activity in a scale of 0 to 1 with an accuracy of 88% with AdaBoost, 94% with SVM, 96% with both HMM and KNN based machine learning techniques.

Storage, retrieval, and communication of body sensor network data

Recently, Body Sensor Networks (BSNs) are being deployed for monitoring and managing medical conditions as well as human performance in sports. These BSNs include various sensors such as accelerometers, gyroscopes, EMG (Electromyogram), EKG (Electro-cardiograms), and other sensors depending on the needs of the medical conditions. Data from these sensors are typically Time Series data and the data from multiple sensors form multiple, multidimensional time series data. Analyzing data from such multiple medical sensors pose several challenges: different sensors have different characteristics, different people generate different patterns through these sensors, and even for the same person the data can vary widely depending on time and environment. This tutorial describes the technologies that go behind BSNs - both in terms of the hardware infrastructure as well as the basic software. First, we outline the BSN hardware features and the related requirements. We then discuss the energy and communication choices for BSNs. Next, we discuss approaches for classification, data mining, visualization, and securing these data. We also show several demonstrations of body sensor networks as well as the software that aid in analyzing the data.

Subject independent computational framework for myoelectric signals

A subject independent computational framework is one which do not require to be calibrated by the specific subject data to be ready to be used on the subject. The greatest challenge in developing such a framework is the variation in parameters across subjects which is termed as subject based variability. Subject based variability is the variability in data across subjects for the same task, activity or physiological condition. Physiological signals are highly subject specific in nature. Myoelectric signals are one such physiological signals generated in the muscles during any musco-skeletal activity of the body. Spectral and amplitude variations in the myoelectric signals are analyzed to determine the physiological status of a muscle with respect to the intensity of activity and the fatigue state of the muscle. But variations in the spectrum and magnitude of myoelectric signals across subjects pose a great challenge in developing a generalized framework for detecting physiological status of the muscle. In this paper we present statistical tools and techniques to measure subject based variability in myoelectric signals and also present a novel feature selection method based on robustness to subject based variability, with the aim of developing a subject independent measurement framework for fatigue using myoelectric signals. The proposed method provides a subject independent classification accuracy of 80.65%, which is an improvement of 10% to 18% compared to the existing techniques when tested with a wide range of classifiers such SVM, HMM, AdaBoost and KNN. More information and source code are available from the authors.