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Real-time vital sensing for persons during exercises is important for effective physical training, healthcare and injury/disease prevention. We have developed a vital sensor node which can sense heart rate (HR), energy expenditure (EE) and body temperature (BT), and furthermore can transmit the sensed data by wireless in real-time, just by hanging itself with the clip at the rim of undershorts. The...
Fatal accidents occur frequently on low-volume rural roads, and the accident rates are up to 4 times higher at curves. It is thus of paramount importance to perform road inventory of rural roads to develop safety plans. However, most states in U.S. face a challenge to maintain a database for low-volume rural roads due to limited funds for road inventory. In this paper, we propose to significantly...
Visually impaired community rely on many artificial aids which allow them to lead an average life in the complex modern society. Identification of currency notes is of utmost importance in this regard and many electronic currency note recognizers have been developed. Nevertheless, a compact, accurate and a cost effective recognizer optimized for local currency notes is highly preferred. In this paper...
We consider the problem of automatic construction of algorithms for recognition of abnormal behavior segments in phase trajectories of dynamic systems. The recognition algorithm is trained on a set of trajectories containing normal and abnormal behavior of the system. The exact position of segments corresponding to abnormal behavior in the trajectories of the training set is unknown. To construct...
Training of laparoscopic surgery in Virtual Reality (VR) environment has been proved as an effective step before clinical practice. Tracking the position of instruments in realtime is an essential part of developing a VR trainer. In this study, we used Microsoft Kinect and color markers instead of using similar traditional means such as mechanical sensors. The orientation and position of instruments...
The Human Activity Recognition is a context awareness application, which has, for example, sports, security and health monitoring applications. As a way to acquire the human activity data, there are external approaches (e.g. cameras data) and embedded approaches (e.g. accelerometer data). In this area, we can find solutions using multiple sensors simultaneously supporting the real time data acquisition...
This study uses machine learning methods to distinguish between healthy and pathological gait. Examples of multi-dimensional pathological and normal gait sequences were collected from post-stroke and healthy individuals in a real clinical setting and with two Kinect sensors. The trajectories of rotational angle and global velocity of selected body joints (hips, spine, shoulders, neck, knees and ankles)...
As one of the several characteristics of hemiplegic patients after stroke, the patients conduct sit-to-stand and stand-to-sit (STS) motion using their unaffected leg. The purpose of this study is to develop a STS motion support system for helping the patients decrease the difference of the usage ratio between the affected side and the unaffected side, and increase the usage of the affected leg. The...
Simultaneous and proportional control of hand and wrist prostheses based upon surface electromyography (myocontrol) is still largely an open issue in the community of assistive robotics. It entails the ability of discriminating the activation levels for each degree of freedom (DOF) of the hand/wrist complex, using as few sensors as possible. Furthermore, one should avoid having the human subject train...
Within the supervised machine learning framework, classifier performance is significantly affected by the size of training datasets. One of the ways to improve classification accuracy with small training datasets is to utilize additional knowledge about training data that is not present in testing data. In the Learning Using Privileged Information (LUPI) learning paradigm, this additional knowledge...
Connected healthcare devices, including medical equipments as well as personal health devices based on emerging mobile technologies used by healthcare providers and consumers, are expanding traditional means of practicing healthcare particularly physiotherapy services. We describe a system based on physiotherapy smart connected devices, as smart walker, smart crutches, force platform and natural user...
Recently, the Optimal Spectral Sampling (OSS) method was implemented in a development version of the Community Radiative Transfer Model (CRTM) at JCSDA. This presentation describes the way that the OSS is implemented in CRTM, and some preliminary evaluation of the performance of the CRTM-OSS in comparison with CRTM-ODPS method. One of the important benefits of the OSS method is its capability to simulate...
One of the complicated issue in compliance control for rehabilitation and assistive robots is to predict right human's motion intention. In this paper, we have proposed an algorithm to estimate Desired Motion Intention (DMI) so that better compliance could be provided by the rehabilitation and assitive robots. Proposed algorithms is based on Extreme Learning Machine (ELM) and takes inputs from different...
In this paper, the soil moisture content (SMC) estimated from Advanced Microwave Scanning Radiometer 2 (AMSR2) through the ANN-based “HydroAlgo” algorithm is firstly compared with the outputs of the Soil Water Balance hydrological model (SWBM). The comparison is performed over Italy, by considering all the available overpasses of AMSR2, since July 2012. The SMC generated by Hydroalgo is then considered...
The problem of place recognition is central to robot navigation. The robot needs to be able to recognize or at least to be able to estimate the likelihood that it has been at a place before when it has returned to a previously visited place. We cast the place recognition problem as one of classifying among multiple linear regression models, and argue that new theory from sparse signal representation...
We investigate the performance of a hybrid, opportunistic/underlay Licensed Shared Access (LSA) system, in the presence of Spectrum Sensing (SS) and Channel Estimation (CE) uncertainties, and derive a simple closed form expression for the system's Bit Error Rate (BER). Based on the derived expression, we introduce an optimization problem for optimally selecting the time allocated to the SS and CE...
Electromyography Pattern Recognition (EMG-PR), a promising control mechanism for upper-limb multifunctional prostheses, has received a great deal of research attention. Despite much research efforts, the clinical performance of multiple degrees of freedom prostheses is not yet satisfactory. One of the possible reasons might be the disparity between current research settings and clinical applications...
The problem of missing samples in road traffic data undermines the performance of intelligent transportation applications. This paper proposes a data-driven imputation method that exploits the spatial and temporal relationships existing between the traffic flows of multiple road segments that are correlated with each other. The K-means clustering technique is used to group together road segments with...
Decision tree and random forest algorithm are introduced to the field of gesture recognition, and the gestures are classified by the fusion information of sEMG and inertial sensor. Experiments show that gesture recognition based on multi fusion information is more accurate than only using surface EMG or inertial signals. Taking the common 12 kinds of gestures as an example, the average recognition...
Efficient spectrum sensing can be realized by predicting the future idle times of primary users' activity in a cognitive radio network. In dynamic spectrum access, based on a reliable prediction scheme, a secondary user chooses a channel with the longest idle time for data transmission. In this paper, four supervised machine learning techniques, two from ANN, i.e. Multilayer Perceptron & Recurrent...
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