Gait anomaly detection with low cost and low resolution infrared sensor arrays

Detecting anomalies in human gait could be used as indicators of human fall risk or other underlying health or psychological issues. This would require collecting reliable gait data. However, collecting human abnormal gait data is very challenging compared to data gathered from normal daily activities mainly because the former are relatively scarce and may exhibit an unmanageable variability with unpredictable combinations of distorted gait patterns. Recently, it was proposed that privacy concerns due to potential misuses of recorded gait images can be alleviated by using the thermal images captured by the low-resolution and low-cost thermal sensor arrays (TSAs). Therefore, to resolve the privacy concerns and data scarcity simultaneously, this paper proposes a Gait Anomaly Detection (GAD), to be created as a one-class classification (OCC) model and implemented as a reconstruction-based autoencoder (AE), while using TSAs to capture the input data. The data scarcity is conveniently addressed since this GAD design, needs only the plentiful ‘normal’ gait of one person of interest (POI) to build its base model. AE’s were deployed since they learn the intricacies of normal gait patterns, with anomaly threshold placed on the reconstruction errors of the training data. The high performance in detecting specific classes of POI’s gait anomalies, achieving impressive mean values across five critical classification metrics—F1-score (95.26%), accuracy (96.20%), precision (92.76%), recall (97.92%), and specificity (95.00%)—demonstrates the model’s feasibility and practicality. The proposed framework can facilitate independent living among the older adults as an individualised data-efficient, privacy-safe, and low-cost approach to GAD

A survey of virtual prototyping techniques for mechanical product development

Repeated, efficient, and extensive use of prototypes is a vital activity that can make the difference between successful and unsuccessful entry of new products into the competitive world market. In this respect, physical prototyping can prove to be very lengthy and expensive, especially if modifications resulting from design reviews involve tool redesign. The availability and affordability of advanced computer technology has paved the way for increasing utilization of prototypes that are digital and created in computer-based environments, i.e. they are virtual as opposed to being physical. The technology for using virtual prototypes was pioneered and adopted initially by large automotive and aerospace industries. Small-to-medium enterprises (SMEs) in the manufacturing industry also need to take virtual prototyping (VP) technology more seriously in order to exploit the benefits. VP is becoming very advanced and may eventually dominate the product development process. However, physical prototypes will still be required for the near future, albeit less frequently. This paper presents a general survey of the available VP techniques and highlights some of the most important developments and research issues while providing sources for further reference. The purpose of the paper is to provide potential SME users with a broad picture of the field of VP and to identify issues and information relevant to the deployment and implementation of VP technology

Smarter maintenance through internet-based condition monitoring with indirect sensing, novelty detection, and XML

In engineering, combining a number of solutions and technologies can result in more effective systems than using only one approach on its own. In particular, it has been shown that in condition monitoring (CM), smarter maintenance systems may be obtained by integrating various sensors together. This paper extends this idea by integrating various non-homogeneous technologies horizontally. The proposed system is an internet-based condition monitoring (e-CM) prototype that can identify abnormal tension in moving belts. It is shown that by applying a classification technique, known as novelty detection, it is possible to decide the status of belt tension by processing the belt vibration signals from an optical sensor (i.e. an indirect sensing approach). A novel method for industrial network communication using XML to create a single standard format for sensor information is also used to link the sensor to the process controller via the internet using the flexible CAN bus technology; this is used together with low-cost microcontrollers with a built-in ethernet link for data acquisition and transmission. The resulting integrated approach is more efficient because: (a) it can reduce waste by minimizing process interruptions caused by direct belt inspection methods while obtaining high detection accuracy (99.67 per cent) and (b) it can provide on-line remote CM that is cost-effective, simple, standardized, and scalable across a wide area and for a relatively large number of sensors. This improvement is especially important when applied to bottleneck processes and critical components

Testing in the incremental design and development of complex products

Testing is an important aspect of design and development which consumes significant time and resource in many companies. However, it has received less research attention than many other activities in product development, and especially, very few publications report empirical studies of engineering testing. Such studies are needed to establish the importance of testing and inform the development of pragmatic support methods. This paper combines insights from literature study with findings from three empirical studies of testing. The case studies concern incrementally developed complex products in the automotive domain. A description of testing practice as observed in these studies is provided, confirming that testing activities are used for multiple purposes depending on the context, and are intertwined with design from start to finish of the development process, not done after it as many models depict. Descriptive process models are developed to indicate some of the key insights, and opportunities for further research are suggested

Application of neural networks for detection of special causes in multivariate statistical process control

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Comprehensive experimental evaluation of a systematic approach for cost effective and rapid design of condition monitoring systems using Taguchi's Method

This paper provides an extensive experimental and analytical evaluation of a previously presented approach to the systematic design of condition monitoring systems for machining operations [1]. The methodology termed Automated Sensor and Signal Processing Selection (ASPS), is based on Taguchi's orthogonal arrays in order to provide cost effective and speedy selection of sensors and signal processing methods that are ultimately used for monitoring process conditions. The evaluation using tool damage in end milling operations shows that ASPS methodology can successfully achieve its objectives without significantly affecting the system's capability for fault detection. The experiments investigate two new types of cutting tools each with three distinct conditions which are processed by four different and independent neural network paradigms - two supervised and two unsupervised. Thus, the results confirm the feasibility and efficiency of the proposed ASPS methodology and show that it can be applied to condition monitoring systems without the need for implementing pattern recognition tools during the design phase