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Objective assessment of gait is important in the treatment and rehabilitation of patients with different diseases. In this paper, we propose a gait evaluation system using the Procrustes and Euclidean distance matrix analysis. We design and develop an android app to collect real time synchronous accelerometer and gyroscope data from two inertial measurement unit sensors through Bluetooth connectivity. The data is collected from 12 young (ten for modeling and two for validation) and 20 older subjects. We analyze the data collected from real world for stride, step, stance, and swing gait features. We validate our method with the measurements of gait features. The generalized Procrustes analysis is used to estimate a standard normal mean gait shape (NMGS) for ten young subjects. Each gait feature of both young and older subjects is then converted to find the best match with the NMGS using the ordinary Procrustes analysis. The shape distance between the NMGS and each gait shape is estimated using Riemannian shape distance, Riemannian size-and-shape distance, Procrustes size-and-shape distance, and root-mean-square deviation. A t-test is performed to provide statistical evidence of gait shape differences between young and older gaits. A mean form, which is considered as a standard normal mean gait form (NMGF), and inter-feature distances are estimated from the set of ten young subjects. The form difference is estimated between the NMGF and individual gaits of young and older. The degree of abnormality is then estimated for individual features and the result is plotted to visualize the feature in a gait. Experimental results demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The link quality protocols of the IEEE 802.15.4 show low performance in terms of reliability in a hospital environment. To achieve the optimal performance of these protocols, this paper proposes a fuzzy logic-based solution using a detailed empirical reliability assessment. This paper provides a solution through four steps: 1) identifying the suitable IEEE 802.15.4 protocols and validating their suitability for patient monitoring systems through simulation in Castalia 3.2 with the OMNet++ platform; 2) providing a detailed discussion and analysis regarding link quality mechanisms used; 3) creating a real-time test bed to perform the empirical experiments to find the actual link quality estimation for different hospital environments; and 4) proposing a fuzzy logic system (FLS), which maps the results of empirical experiments with the proposed FLS to obtain the optimal results. For empirical experiments, we divide the communication in the hospital into four environmental scenarios, including inside ward, corridor, ward to corridor, and ward to ward. Both mobile and static scenarios are considered with line of sight and non-line of sight. Different link quality threshold values for link quality indicator (LQI) and received signal strength indicator (RSSI) are found for the variety of hospital scenarios. A strong correlation is noticed between LQI and packet reception rate in most of the scenarios, whereas a weak correlation is found between RSSI and packet reception rate. Finally, the results are mapped to our proposed FLS to obtain the optimal performance. [ABSTRACT FROM AUTHOR]

This paper studies the distributed adaptive fuzzy containment tracking control for a class of high-order stochastic pure-feedback nonlinear multiagent systems with multiple dynamic leaders and performance constraint requirement. The control inputs are quantized by hysteresis quantizers. Mean value theorems are used to transfer the nonaffine systems into affine forms and a nonlinear decomposition is employed to solve the quantized input control problem. With a novel structure barrier Lyapunov function, the distributed control strategy is developed. It is strictly proved that the outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders, the containment tracking errors satisfy the performance constraint requirement and the resulting leader-following multiagent system is stable in probability based on Lyapunov stability theory. At last, simulation is provided to show the validity and the advantages of the proposed techniques. [ABSTRACT FROM AUTHOR]

Sensor-based human activity recognition (HAR) has received considerable attention due to its wide applications in health care. Each sensor modality has its advantages and limitations. Single sensor modalities sometimes may not cope with complex situations in practice. To resolve this challenge, we design and develop a practical hybrid sensory HAR system for older people. To enhance the performance of the system, we propose a unique data fusion method through combining both wearable sensors and ambient sensors. The wearable sensors in this paper are used for identifying the specific daily activities. The ambient sensors delivering the occupant’s room-level daily routine provide a more comprehensive surveillance with the wearable sensors together; meanwhile, the captured room-level location information is also used in the data fusion to trigger the sub classification models pretrained by wearable data. We also explore a new feature set extracted from wearable sensors to improve the system performance. We experimentally evaluate our system by applying four typical mutual information-based feature selection methods and the support vector machines classification algorithm instead of other complex algorithms, with the aim of exploring a practical way to improve recognition accuracy. The ground-truth data are gathered from 21 subjects, including 17 daily activities with the sample size of 2,142,000. The experimental results demonstrate the effectiveness of our method. The new feature set help improve the accuracy to 96.82% ± 0.15 from 89.81% ± 0.54 using wearable data only; and the data fusion with ambient information achieves a further increased accuracy of 98.32%. [ABSTRACT FROM AUTHOR]

Our aim is to maximize the interpretable information for gait analysis. To achieve this, it is important to find the optimal sensor placement and the parameters that influence the extraction of automatic gait features. We investigated the effect of different anatomical foot locations on inertial measurement unit (IMU) sensor output. We designed and developed an android app to collect real-time synchronous sensor output. We selected a set of five anatomical foot locations covering most of the foot regions to place wearable IMU sensors for data collection. Each participant performed a trial in a straight corridor comprising 25 strides of normal walking, a turn-around, and another 25 strides. We proposed an automatic gait features extraction method to analyze the data for stride number, distance, speed, length and period of stride, stance, and swing phases during walking. The highest accuracy for detecting stride number was in location 1 (first cuneiform) followed by location 5 (Achilles Tendon) and 4 (Talus). Location 1 was the closest to correlate estimate to the measured distance travelled. The accuracy of detecting number of strides on average is 95.47% from accelerometer data and 93.60% from gyroscope data and closest to the 60:40% split for average stance and swing for 15 subjects. To validate our results, we conducted trials using the Qualisys motion capture instrument and from our sensors concurrently. The average accuracy of the result is 97.77% with 95% confidence interval 0.767 for estimated and 99.01% with 95% confidence interval 0.266 for period. [ABSTRACT FROM PUBLISHER]