コンピュータ・サイエンス : ACM computing surveys

Staying at the top is getting tougher and more challenging due to the fast-growing and changing digital technologies and AI-based solutions. The world of technology, mass customization, and advanced manufacturing is experiencing a rapid transformation. Robots are becoming even more important as they can now be coupled with the human mind by means of brain–machine interface and advances in artificial intelligence. A strong necessity to increase productivity while not removing human workers from the manufacturing industry is imposing punishing challenges on the global economy. To counter these challenges, this article introduces the concept of Industry 5.0, where robots are intertwined with the human brain and work as collaborator instead of competitor. This article also outlines a number of key features and concerns that every manufacturer may have about Industry 5.0. In addition, it presents several developments achieved by researchers for use in Industry 5.0 applications and environments. Finally, the impact of Industry 5.0 on the manufacturing industry and overall economy is discussed from an economic and productivity point of view, where it is argued that Industry 5.0 will create more jobs than it will take away.

Industry 5.0—A Human-Centric Solution

A survey on ECG analysis

This paper describes the world's largest gait database-the “OU-ISIR Gait Database, Large Population Dataset”-and its application to a statistically reliable performance evaluation of vision-based gait recognition Whereas existing gait databases include at most 185 subjects, we construct a larger gait database that includes 4007 subjects (2135 males and 1872 females) with ages ranging from 1 to 94 years The dataset allows us to determine statistically significant performance differences between currently proposed gait features In addition, the dependences of gait-recognition performance on gender and age group are investigated and the results provide several novel insights, such as the gradual change in recognition performance with human growth

/pdf/the-ou-isir-gait-database-comprising-the-large-population-3ra12v92eg.pdf

The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition

Deep-ECG: Convolutional Neural Networks for ECG biometric recognition

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field.

/pdf/a-review-of-vision-based-gait-recognition-methods-for-human-3dcnqtrtup.pdf

A Review of Vision-Based Gait Recognition Methods for Human Identification

Bag-of-words representation for biomedical time series classification

This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments Specifically, local segments extracted from an ECG signal are projected to a small number of basic elements in a dictionary, which is learned from training data A final representation is extracted by performing a max pooling procedure over all the sparse coefficient vectors in the ECG signal Unlike most of existing methods for human identification from ECG signals which require segmentation of individual heartbeats or extraction of fiducial points, the proposed method does not need to segment individual heartbeats or detect any fiducial points The method achieves an 9948% accuracy on a 100 subjects dataset constructed from a publicly available database, which demonstrates that both local and global structural information are well captured to characterize the ECG signals

Human Identification From ECG Signals Via Sparse Representation of Local Segments

Recognizing Human Daily Activities From Accelerometer Signal

Knowledge of when, how, and by whom water is being used is crucial for planning ways to conserve drinking water. The goal of this paper is to identify groups of similar households (whom) based on their regular high-magnitude behaviors (RHMBs) of water consumption (when and how). RHMBs are frequent recurrences of high water use with regular timing. Household RHMBs are promising targets for behavior change. A two-stage data analytics approach is proposed. First, smart meter data is analyzed to identify RHMBs automatically. Second, salient features of the RHMBs are used to group households with similar behaviors. The approach is evaluated on two contrasting towns from low-rainfall regions of Australia. RHMBs accounted for 2 to 10 times more water than the traditional water efficiency target of continuous flows. For one group of 220 households, 60% of peak-hour demand was RHMBs. This paper demonstrates how RHMBs can be used to pinpoint opportunities for tailored demand management. Targets for substant...

Jin Wang

Papers

A Review of Vision-Based Gait Recognition Methods for Human Identification

Bag-of-words representation for biomedical time series classification

Human Identification From ECG Signals Via Sparse Representation of Local Segments

Recognizing Human Daily Activities From Accelerometer Signal

Smart Meter Analytics to Pinpoint Opportunities for Reducing Household Water Use