MGait : Model-Based Gait Analysis Using Wearable Bend and Inertial Sensors

Movement disorders, such as Parkinson’s disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step and stride lengths provide valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, this article presents a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. Experimental results show that the proposed model estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.

Seasonal Accumulated Workloads in Collegiate Women’s Soccer: A Comparison of Starters and Reserves

Research quantifying the unique workload demands of starters and reserves in training and match settings throughout a season in collegiate soccer is limited. Purpose: The purpose of the current study is to compare accumulated workloads between starters and reserves in collegiate soccer. Methods: Twenty-two NCAA Division III female soccer athletes (height: 1.67 ± 0.05 m; body mass: 65.42 ± 6.33 kg; fat-free mass: 48.99 ± 3.81 kg; body fat %: 25.22 ± 4.78%) were equipped with wearable global positioning systems with on-board inertial sensors, which assessed a proprietary training load metric and distance covered for each practice and 22 matches throughout an entire season. Nine players were classified as starters (S), defined as those playing >50% of playing time throughout the entire season. The remaining 17 were reserves (R). Goalkeepers were excluded. A one-way ANOVA was used to determine the extent of differences in accumulated training load throughout the season by player status. Results: Accumulated training load and total distance covered for starters were greater than reserves ((S: 9431 ± 1471 vs. R: 6310 ± 2263 AU; p < 0.001) and (S: 401.7 ± 31.9 vs. R: 272.9 ± 51.4 km; p < 0.001), respectively) throughout the season. Conclusions: Starters covered a much greater distance throughout the season, resulting in almost double the training load compared to reserves. It is unknown if the high workloads experienced by starters or the low workloads of the reserves is more problematic. Managing player workloads in soccer may require attention to address potential imbalances that emerge between starters and reserves throughout a season.

Human Activity Recognition Based on Residual Network and BiLSTM

Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.

Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

This study aims to build a system for detecting a driver’s internal state using body-worn sensors. Our system is intended to detect inattentive driving that occurs during long-term driving on a monotonous road, such as a high-way road. The inattentive state of a driver in this study is an absent-minded state caused by a decrease in driver vigilance levels due to fatigue or drowsiness. However, it is difficult to clearly define these inattentive states because it is difficult for the driver to recognize when they fall into an absent-minded state. To address this problem and achieve our goal, we have proposed a detection algorithm for inattentive driving that not only uses a heart rate sensor, but also uses body-worn inertial sensors, which have the potential to detect driver behavior more accurately and at a much lower cost. The proposed method combines three detection models: body movement, drowsiness, and inattention detection, based on an anomaly detection algorithm. Furthermore, we have verified the accuracy of the algorithm with the experimental data for five participants that were measured in long-term and monotonous driving scenarios by using a driving simulator. The results indicate that our approach can detect both the inattentive and drowsiness states of drivers using signals from both the heart rate sensor and accelerometers placed on wrists.

Design of the Capacitive Dual-Mass Micromechanical Gyroscope

Development of micromechanical inertial sensors have made it possible to use them in the navigation and motion control systems. This application area imposes strict requirements on sensors. One of the ways to meet the requirements and to improve the gyroscope characteristics is to apply a dual- or multi-mass architecture of a gyroscope sensing element. This paper presents the results of dual-mass micromechanical gyroscope with a measurement range of ±450°/s design. The complex design method, including simulation at the system level, model refinement based on the results of finite element modelling, and modelling of individual electronic blocks at the circuit level, is described.

Background-oriented schlieren technique in experimental research regarding the effect of explosion pressure

In the following paper, experimental results regarding the effect of explosion pressure are obtained from open field experiments with detonation of explosive charges. In addition, sensors that can be used for security applications for the detection of toxic and explosive compounds, as well as mobile systems for the detection of shock waves due to explosions were used to acquire more detailed results. Sensors are the main components in products and systems used to detect chemicals and volatile organic compounds (VOCs) targeting applications in several fields, such as: industrial production and the automotive industry (detection of polluting gases from cars, medical applications, indoor air quality control. The sensory characteristics of a robot depend very much on its degree of autonomy, the applications for which it was designed and the type of work environment. The sensors can be divided into two categories: internal status sensors (sensors that provide information about the internal status of the mobile robot); external status sensors (sensors that provide information about the environment in which the robot operates). Another classification of these could be: distance sensors, position sensors, environmental sensors - sensors that provide information about various properties and characteristics of the environment (example: temperature, pressure, color, brightness), inertial sensors.