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Algorithms
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Algorithms for Psycho-Motor Training and Performance Using Wearable Technologies
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Algorithms for Psycho-Motor Training and Performance Using Wearable Technologies

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 May 2016) | Viewed by 18921

Special Issue Editor

Prof. Dr. Olga C. Santos

E-Mail Website
Guest Editor
aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, 28040 Madrid, Spain
Interests: recommender systems; affective computing; user modelling; human computer interaction; personalized interaction; human activity recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Algorithms aims to compile original research on algorithms for sensing, processing and/or supporting psycho-motor training. Current advances on wearable technologies (such as intelligent bracelets, watches, t-shirts, etc.) and the interconnection capabilities provided by the Internet of Things can facilitate quantifying and describing the activity of a person while carrying out learning tasks that require consolidating motor tasks into memory through repetition towards accurate movements (e.g., playing a musical instrument, practicing a sport technique, using sign language, etc.). In fact, wearable devices can be used for sensing cognitive, affective, and physical activity. Thus, algorithms can provide support for embodied cognition, affection, and motor skills through varied feedback provided through diverse types of actuators that can take advantage of ambient intelligence (to get to the learner through the most appropriate sensorial channels in a low intrusive way) and/or embodiment scaffolding (e.g., exoskeletons). In this context, this Special Issue invites proposals that provide algorithmic solutions to address some of the existing challenges in terms of the modeling of the physical interaction of the person and the provision of the required personalized support during the physical training. Some of these challenges relate to wearable sensor data analysis, mining, aggregating, modeling, visualizing, sharing, securing, querying, etc., in a distributed way. The mapping of algorithms input/output to interconnectivity standards in order to take advantage of available distributed processing infrastructures would also be needed. The benefits (or not) of Big Data processing algorithms on human movements data sensed needs also to be explored.

Dr. Olga C. Santos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


Keywords

  • Wearable devices
  • Streaming of sensed data
  • Data mining
  • Time series analysis
  • Wireless and sensor networks
  • Big data processing
  • Cloud computing
  • Exoskeletons
  • Internet of Things
  • Ambient intelligence

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Published Papers (3 papers)

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5641 KiB  
Article
An Architectural Based Framework for the Distributed Collection, Analysis and Query from Inhomogeneous Time Series Data Sets and Wearables for Biofeedback Applications
by James Lee, David Rowlands, Nicholas Jackson, Raymond Leadbetter, Tomohito Wada and Daniel A. James
Algorithms 2017, 10(1), 23; https://doi.org/10.3390/a10010023 - 1 Feb 2017
Cited by 1 | Viewed by 4765
Abstract
The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. [...] Read more.
The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: “Quantifying movement demands of AFL football using GPS tracking”). This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming. Full article
(This article belongs to the Special Issue Algorithms for Psycho-Motor Training and Performance Using Wearable Technologies)
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688 KiB  
Article
Toward Personalized Vibrotactile Support When Learning Motor Skills
by Olga C. Santos
Algorithms 2017, 10(1), 15; https://doi.org/10.3390/a10010015 - 16 Jan 2017
Cited by 11 | Viewed by 5627
Abstract
Personal tracking technologies allow sensing of the physical activity carried out by people. Data flows collected with these sensors are calling for big data techniques to support data collection, integration and analysis, aimed to provide personalized support when learning motor skills through varied [...] Read more.
Personal tracking technologies allow sensing of the physical activity carried out by people. Data flows collected with these sensors are calling for big data techniques to support data collection, integration and analysis, aimed to provide personalized support when learning motor skills through varied multisensorial feedback. In particular, this paper focuses on vibrotactile feedback as it can take advantage of the haptic sense when supporting the physical interaction to be learnt. Despite each user having different needs, when providing this vibrotactile support, personalization issues are hardly taken into account, but the same response is delivered to each and every user of the system. The challenge here is how to design vibrotactile user interfaces for adaptive learning of motor skills. TORMES methodology is proposed to facilitate the elicitation of this personalized support. The resulting systems are expected to dynamically adapt to each individual user’s needs by monitoring, comparing and, when appropriate, correcting in a personalized way how the user should move when practicing a predefined movement, for instance, when performing a sport technique or playing a musical instrument. Full article
(This article belongs to the Special Issue Algorithms for Psycho-Motor Training and Performance Using Wearable Technologies)
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1649 KiB  
Article
HMM Adaptation for Improving a Human Activity Recognition System
by Rubén San-Segundo, Juan M. Montero, José Moreno-Pimentel and José M. Pardo
Algorithms 2016, 9(3), 60; https://doi.org/10.3390/a9030060 - 2 Sep 2016
Cited by 13 | Viewed by 7867
Abstract
When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR) system. [...] Read more.
When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR) system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs). This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down) using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity), and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP) approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance. Full article
(This article belongs to the Special Issue Algorithms for Psycho-Motor Training and Performance Using Wearable Technologies)
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