In recent years, due to changing the human lifestyle, the number of sport trainers has been increased. The conventional classifiers as Naive Bayes (NB), Decision Trees (DT) and Convolutional Neural ...Networks (CNNs) can be used in this domain to recognize and count sports activities of subjects and provide them qualified feedback. This paper uses literature studies and selected sport activities, namely squats, pull-ups and dips as the dataset based on three UWB sensors with additional inertial data, which contains the reduced data set consisting of 17 training sets and next for CNN training the 1444 samples describing exercises and 2024 samples with breaks, which were grouped in the ratio 70:15:15. The recognition accuracy of the NB and DT were 89.4 and 92.9 accordingly. Next, the extensive performance analysis of the CNN based on experiments for different kernel sizes, different number of filters for single and dual layer networks was carried out. Moreover, the innovative model for sport activities recognition in the form the combination of several networks forming Ensemble Neural Network (ENN) was created. The accuracy was at the level 94.81 of CNN and exceeded 95% of ENN. The proposed prototype of the measurement system and data acquisition platform for sport activities recognition was highlighted as the great potential in the privacy-training sport system.
Display omitted
•The approachto the measurement system for sport activities: squats, pull-ups and dipsrecognition was developed.•The developedmodel for sport activities recognition using Ensemble Neural Network (ENN)was created.•The testingaccuracy in sport activity recognition exceeded 95%.•The great potential in the privacy-training of theproposed system was highlighted.
The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, ...three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user's sport activity.
We studied the use of a rotating multi-layer 3D Light Detection And Ranging (LiDAR) sensor (specifically the Velodyne HDL-32E) mounted on a social robot for the estimation of features of people ...around the robot. While LiDARs are often used for robot self-localization and people tracking, we were interested in the possibility of using them to estimate the people's features (states or attributes), which are important in human-robot interaction. In particular, we tested the estimation of the person's body orientation and their gender. As collecting data in the real world and labeling them is laborious and time consuming, we also looked into other ways for obtaining data for training the estimators: using simulations, or using LiDAR data collected in the lab. We trained convolutional neural network-based estimators and tested their performance on actual LiDAR measurements of people in a public space. The results show that with a rotating 3D LiDAR a usable estimate of the body angle can indeed be achieved (mean absolute error 33.5 ° ), and that using simulated data for training the estimators is effective. For estimating gender, the results are satisfactory (accuracy above 80%) when the person is close enough; however, simulated data do not work well and training needs to be done on actual people measurements.
Reminding is often identified as a central function of socially assistive robots in the healthcare sector. The robotic reminders are supposed to help people with memory impairments to remember to ...take their medicine, to drink and eat, or to attend appointments. Such standalone reminding technologies can, however, be too demanding for people with memory injuries. In a co-creation process, we developed an individual reminder robot together with a person with traumatic brain injury and her care personnel. During this process, we learned that while current research describe reminding as a prototypical task for socially assistive robots, there is no clear definition of what constitutes a reminder nor that it is based on complex sequences of interactions that evolve over time and space, across different actions, actors and technologies. Based on our data from the co-creation process and the first deployment, we argue for a shift towards a sequential and socially distributed character of reminding. Understanding socially assistive robots as rehabilitative tools for people with memory impairment, they need to be reconsidered as interconnected elements in institutional care practices instead of isolated events for the remindee.
Supervised learning as a sub-discipline of machine learning enables the recognition of correlations between input variables (features) and associated outputs (classes) and the application of these to ...previously unknown data sets. In addition to typical areas of application such as speech and image recognition, fields of applications are also being developed in the sports and fitness sector. The purpose of this work was to implement a workflow for the automated recognition of sports exercises in the Matlab® programming environment and to carry out a comparison of different model structures. First, the acquisition of the sensor signals provided in the local network and their processing were implemented. The functionalities to be realised included the interpolation of lossy time series, the labelling of the activity intervals performed and, in part, the generation of sliding windows with statistical parameters. The preprocessed data were used for the training of classifiers and artificial neural networks (ANN). These were iteratively optimised in their corresponding hyper parameters for the data structure to be learned. The most reliable models were finally trained with an increased data set, validated and compared with regard to the achieved performance. In addition to the usual evaluation metrics such as F1 score and accuracy, the temporal behaviour of the assignments was also displayed graphically, which enabled statements to be made about potential causes for incorrect assignments. In this context, especially the transition areas between the classes were detected as erroneous assignments as well as exercises with insufficient or clearly deviating execution. The best overall accuracy achieved with ANN and the increased dataset was 93.7 %.
Given the use of high-strength steels to achieve lightweight construction goals, conventional shear-cutting processes are reaching their limits. Therefore, so-called high-speed impact cutting (HSIC) ...is used to achieve the required cut surface qualities. A new machine concept consisting of linear motors and an impact mass is presented to investigate HSIC. It allows all relevant parameters to be flexibly adjusted and measured. The design and construction of the test bench, as well as the mechanism for coupling the impact mass, are described. To validate the theoretically determined process speeds, the cutting process was recorded with high-speed cameras, and HSIC with a mild deep-drawing steel sheet was performed. It was discovered that very good cutting edges could be produced, which showed a significantly lower hardening depth than slowly cut reference samples. In addition, HSIC was numerically modelled in LS-DYNA, and the calculated cutting edges were compared with the real ones. With the help of adaptive meshing, a very good agreement for the cutting edges could be achieved. The results show the great potential of using a linear motor in HSIC.
People with cognitive impairments have limited social abilities, and their social relations often rely on other people people taking initiative. Therefore, they need social learning to be able to ...socially engage with others. This project accommodates this need by promoting social interactions in a smart learning ecosystem for cognitively impaired adolescents at a rehabilitation centre in Denmark. In collaboration with the staff and residents at the facility, we developed together with staff and residents a music game prototype. The basic functionality includes two users playing virtual music instruments by using gestures and body movement. To support criteria established from the users, the game is designed to induce physical, cognitive and social learning in a diffused learning space. The study measured the intersubjective interactions between the residents when playing the game and found that verbal encouragements from the system affected their interactions. The staff members reported that the game has strong motivational properties for the residents in doing physical movements and interacting with each other.
The article begins by presenting an overview of the contents of this journal that relate to the five Intangible Heritage domains identified by UNESCO. A model for digitising Intangible Heritage is ...presented (Tripartite Digitisation Model) and further explained by surveying and including articles from the Journal. Finally, the article discusses the implications and facilitation of digitisation with the participation of indigenous communities.
PDF
