Optimization of Forcemyography Sensor Placement for Arm Movement Recognition

How to design an optimal wearable device for human movement recognition is vital to reliable and accurate human-machine collaboration. Previous works mainly fabricate wearable devices heuristically. Instead, this paper raises an academic question: can we design an optimization algorithm to optimize the fabrication of wearable devices such as figuring out the best sensor arrangement automatically? Specifically, this work focuses on optimizing the placement of Forcemyography (FMG) sensors for FMG armbands in the application of arm movement recognition. Firstly, based on graph theory, the armband is modeled considering sensors' signals and connectivity. Then, a Graph-based Armband Modeling Network (GAM-Net) is introduced for arm movement recognition. Afterward, the sensor placement optimization for FMG armbands is formulated and an optimization algorithm with greedy local search is proposed. To study the effectiveness of our optimization algorithm, a dataset for mechanical maintenance tasks using FMG armbands with 16 sensors is collected. Our experiments show that using only 4 sensors optimized with our algorithm can help maintain a comparable recognition accuracy to using all sensors. Finally, the optimized sensor placement result is verified from a physiological view. This work would like to shed light on the automatic fabrication of wearable devices considering downstream tasks, such as human biological signal collection and movement recognition. Our code and dataset are available at https://github.com/JerryX1110/IROS22-FMG-Sensor-OptimizationComment: 6 pages, 10 figures, Accepted by IROS22 (The 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection

In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite significant progress, these methods still suffer from unsatisfactory results such as numerous false alarms, primarily due to the absence of precise temporal anomaly annotation. In this paper, we present a novel labeling paradigm, termed "glance annotation", to achieve a better balance between anomaly detection accuracy and annotation cost. Specifically, glance annotation is a random frame within each abnormal event, which can be easily accessed and is cost-effective. To assess its effectiveness, we manually annotate the glance annotations for two standard video anomaly detection datasets: UCF-Crime and XD-Violence. Additionally, we propose a customized GlanceVAD method, that leverages gaussian kernels as the basic unit to compose the temporal anomaly distribution, enabling the learning of diverse and robust anomaly representations from the glance annotations. Through comprehensive analysis and experiments, we verify that the proposed labeling paradigm can achieve an excellent trade-off between annotation cost and model performance. Extensive experimental results also demonstrate the effectiveness of our GlanceVAD approach, which significantly outperforms existing advanced unsupervised and weakly supervised methods. Code and annotations will be publicly available at https://github.com/pipixin321/GlanceVAD.Comment: 21 page

Logic, Rationality, and Interaction4th International Workshop, LORI 2013, Hangzhou, China, October 9-12, 2013, Proceedings /

XII, 363 p. 41 illus.online resource

Promoting Healthy Village Construction: Challenges and Countermeasures

Healthcare is an important component for poverty alleviation in China, and it requires constant efforts as this sector in the rural areas in China will remain underdeveloped and short of high-quality resources for a long time. Moreover, promoting healthy village construction is crucial for consolidating and expanding the key achievements obtained in poverty alleviation and for implementing the rural revitalization strategy in China. This study uses policy research, field research, data analysis, and expert discussion methods. We first summarize the practical needs for promoting healthy village construction and present the achievements and main problems regarding healthcare improvement for poverty alleviation. Subsequently, we explore the development objectives and key tasks for healthy village construction and propose several countermeasures prospectively. To prevent the population that have been lifted out of poverty from returning to it due to illness and better meet their diverse needs for health, we suggest that China should (1) increase government financial investment and scientifically optimize the layout of health resources and human resources; (2) provide health services based on the whole life cycle and the whole process of health; (3) maximize the unique advantages of traditional Chinese medicine to draw a bottom line for epidemic prevention and control in rural areas; (4) ensure drug security based on the healthcare service coordination mechanism within the country region; (5) establish a regional adjustment and balancing mechanism for medical insurance funds to ensure the accuracy and fairness of health policies; and (6) conduct rural doctors training programs

The Optimal Configuration Scheme of the Virtual Power Plant Considering Benefits and Risks of Investors

A virtual power plant (VPP) is a special virtual unit that integrates various distributed energy resources (DERs) distributed in the generation and consumption sides. The optimal configuration scheme of the VPP needs to break the geographical restrictions to make full use of DERs, considering the uncertainties. First, the components of the DERs and the structure of the VPP are briefly introduced. Next, the cubic exponential smoothing method is adopted to predict the VPP load requirement. Finally, the optimal configuration of the DER capacities inside the VPP is calculated by using portfolio theory and genetic algorithms (GA). The results show that the configuration scheme can optimize the DER capacities considering uncertainties, guaranteeing economic benefits of investors, and fully utilizing the DERs. Therefore, this paper provides a feasible reference for the optimal configuration scheme of the VPP from the perspective of investors

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Previous studies have investigated various factors that contribute to the severity of work zone crashes. However, little has been done on the specific effects of light conditions. Using the data from the Enhanced Tennessee Roadway Information Management System (E-TRIMS), crashes that occurred in the Tennessee work zones during 2003–2015 are categorized into three light conditions: daylight, dark-lighted, and dark-not-lighted. One commonly used decision tree method—Classification and Regression Trees (CART)—is adopted to investigate the factors contributing to crash severity in highway work zones under these light conditions. The outcomes from the three decision trees with differing light conditions show significant differences in the ranking and importance of the factors considered in the study, thereby indicating the necessity of examining traffic crashes according to light conditions. By separately considering the crash characteristics under different light conditions, some new findings are obtained from this study. The study shows that an increase in the number of lanes increases the crash severity level in work zones during the day while decreasing the severity at night. Similarly, drugs and alcohol are found to increase the severity level significantly under the dark-not-lighted condition, while they have a limited influence under daylight and dark-lighted conditions

sEMG-Based Hand-Gesture Classification Using a Generative Flow Model

Conventional pattern-recognition algorithms for surface electromyography (sEMG)-based hand-gesture classification have difficulties in capturing the complexity and variability of sEMG. The deep structures of deep learning enable the method to learn high-level features of data to improve both accuracy and robustness of a classification. However, the features learned through deep learning are incomprehensible, and this issue has precluded the use of deep learning in clinical applications where model comprehension is required. In this paper, a generative flow model (GFM), which is a recent flourishing branch of deep learning, is used with a SoftMax classifier for hand-gesture classification. The proposed approach achieves 63.86 ± 5.12 % accuracy in classifying 53 different hand gestures from the NinaPro database 5. The distribution of all 53 hand gestures is modelled by the GFM, and each dimension of the feature learned by the GFM is comprehensible using the reverse flow of the GFM. Moreover, the feature appears to be related to muscle synergy to some extent