A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition

BACKGROUND: Unreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. While the proposed SFTM has shown great promise, the previous design is impractical. A practical SFTM has to be fast enough, lightweight, automatic, and robust under different conditions with or without disturbances. METHODS: This paper presented a real-time, practical SFTM towards robust EMG PR. A novel fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect disturbances and recover the PR performance immediately. These components of SFTM were then integrated with the EMG PR module and tested on five able-bodied subjects and a transradial amputee in real-time for classifying multiple hand and wrist motions under different conditions with different disturbance types and levels. RESULTS: The proposed fast LDA retraining algorithm significantly shortened the retraining time from nearly 1 s to less than 4 ms when tested on the embedded system prototype, which demonstrated the feasibility of a nearly “zero-delay” SFTM that is imperceptible to the users. The results of the real-time tests suggested that the SFTM was able to handle different types of disturbances investigated in this study and significantly improve the classification performance when one or multiple EMG signals were disturbed. In addition, the SFTM could also maintain the system’s classification performance when there was no disturbance. CONCLUSIONS: This paper presented a real-time, lightweight, and automatic SFTM, which paved the way for reliable and robust EMG PR for prosthesis control

A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition

BackgroundUnreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. While the proposed SFTM has shown great promise, the previous design is impractical. A practical SFTM has to be fast enough, lightweight, automatic, and robust under different conditions with or without disturbances.MethodsThis paper presented a real-time, practical SFTM towards robust EMG PR. A novel fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect disturbances and recover the PR performance immediately. These components of SFTM were then integrated with the EMG PR module and tested on five able-bodied subjects and a transradial amputee in real-time for classifying multiple hand and wrist motions under different conditions with different disturbance types and levels.ResultsThe proposed fast LDA retraining algorithm significantly shortened the retraining time from nearly 1s to less than 4ms when tested on the embedded system prototype, which demonstrated the feasibility of a nearly “zero-delay” SFTM that is imperceptible to the users. The results of the real-time tests suggested that the SFTM was able to handle different types of disturbances investigated in this study and significantly improve the classification performance when one or multiple EMG signals were disturbed. In addition, the SFTM could also maintain the system’s classification performance when there was no disturbance.ConclusionsThis paper presented a real-time, lightweight, and automatic SFTM, which paved the way for reliable and robust EMG PR for prosthesis control

City Sustainable Development Evaluation Based on Hesitant Multiplicative Fuzzy Information

Sustainable development evaluation is the basis of city sustainable development research, and effective evaluation is the foundation for guiding the formulation and implementation of sustainable development strategy. In this paper, we provided a new city sustainable development evaluation method called hesitant multiplicative fuzzy TODIM (HMF-TODIM). The main advantage of this method is that it can deal with the subjective preference information of the decision-makers. The comparison study of existing methods and HMF-TODIM is also carried out. Additionally, real case analysis is presented to show the validity and superiority of the proposed method. Research results in this paper can provide useful information for the construction of sustainable cities

Implementing an FPGA system for real-time intent recognition for prosthetic legs

ABSTRACT This paper presents the design and implementation of a cyber physical system (CPS) for neural-machine interface (NMI) that continuously senses signals from a human neuromuscular control system and recognizes the user's intended locomotion modes in real-time. The CPS contains two major parts: a microcontroller unit (MCU) for sensing and buffering input signals and an FPGA device as the computing engine for fast decoding and recognition of neural signals. The real-time experiments on a human subject demonstrated its real-time, self-contained, and high accuracy in identifying three major lower limb movement tasks (level-ground walking, stair ascent, and standing), paving the way for truly neuralcontrolled prosthetic legs

The Early Stage Wheel Fatigue Crack Detection Using Eddy Current Pulsed Thermography

The in-service wheel-set quality is one of critical challenges for railway safety, especially for the high-speed train. The defect in wheel tread, initiated by rolling contact fatigue (RCF) damage, is one of the most significant phenomena and has serious influence on rail industry. Eddy current pulsed thermography (ECPT) is studied to compensate the Ultrasonic Testing (UT) method for detection these early stage of fatigue cracks in wheel tread. This paper proposes several induction coils, such as linear coil, Yoke coil and Helmholtz coils, based ECPT method to meet the imaging of multiple cracks and irregular surface in wheel tread through numerical simulation and experimental results. Some features are extracted and studied also to quantify the fatigue crack in term of UT and ECPT. The proposed method greatly enhances the capability for cracks detection and quantitative evaluation compared with previous Non-Destructive Testing (NDT) method in railway

On Design and Implementation of Neural-Machine Interface for Artificial Legs

The quality-of-life of leg amputees can be improved dramatically by using a cyber-physical system (CPS) that controls artificial legs based on neural signals representing amputees\u27 intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system-a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user\u27s intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a postprocessing scheme, was developed to identify the user\u27s intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real-time testing. Real-time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs

Federated Deep Multi-View Clustering with Global Self-Supervision

Federated multi-view clustering has the potential to learn a global clustering model from data distributed across multiple devices. In this setting, label information is unknown and data privacy must be preserved, leading to two major challenges. First, views on different clients often have feature heterogeneity, and mining their complementary cluster information is not trivial. Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data. To address these challenges, we propose a novel federated deep multi-view clustering method that can mine complementary cluster structures from multiple clients, while dealing with data incompleteness and privacy concerns. Specifically, in the server environment, we propose sample alignment and data extension techniques to explore the complementary cluster structures of multiple views. The server then distributes global prototypes and global pseudo-labels to each client as global self-supervised information. In the client environment, multiple clients use the global self-supervised information and deep autoencoders to learn view-specific cluster assignments and embedded features, which are then uploaded to the server for refining the global self-supervised information. Finally, the results of our extensive experiments demonstrate that our proposed method exhibits superior performance in addressing the challenges of incomplete multi-view data in distributed environments

Life-Cycle Building Carbon Emission Management Platform based on Building Information Modeling Technology

Buildings produce 40% of annual carbon emissions among various sectors in modern society. One of the most challenging problems of carbon management is how to monitor and calculate a building’s life-cycle energy consumption and carbon emission data during both construction and operation stages. The Building Information Modeling (BIM) technology provides a promising method to obtain and simulate buildings as-is status at different stages in the life cycle. This paper develops a framework for building a carbon emission management platform using the carbon emission factor method and BIM technology, which can derive corresponding carbon emission and measure carbon footprint with building geographic information to achieve precise positioning of carbon emission objects. The platform can achieve multi-role collaboration, equipment visualization, real-time carbon emission monitoring, and data analysis. The platform is applied to an existing building in Hohai University to assess the total carbon footprint of the building in its life cycle. This platform can greatly improve the calculation accuracy of the carbon footprint of buildings, improve data transparency, provide valuable information for building facility management personnel, and help achieve the goal of carbon neutrality

Influence of Heat Treatment on the Morphologies of Copper Nanoparticles Based Films by a Spin Coating Method

We have investigated the influence of heat treatment on the morphologies of copper nanoparticles based films on glass slides by a spin coating method. The experiments show that heat treatment can modify the sizes and morphologies of copper nanoparticles based films on glass slides. We suggest that through changing the parameters of heat treatment process may be helpful to vary the scattering and absorbing intensity of copper nanoparticles when used in energy harvesting/conversion and optical devices