Special Issue "Sensor-Based Activity Recognition and Interaction"

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (28 February 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Thomas Kirste

Institute of Computer Science, University of Rostock, 18051 Rostock, Germany
Website | E-Mail
Interests: mobile multimedia information systems; intelligent environments; activity recognition and annotation; agent-based approaches; technically-assisted rehabilitation
Guest Editor
Prof. Dr. Bodo Urban

Fraunhofer IGD Rostock, Joachim-Jungius-Straße 11, 18059 Rostock, Germany
Website | E-Mail
Phone: +49 381 4024-110
Interests: multimedia communication; healthcare analytics; wearable interaction; internet of things; smart factories
Guest Editor
Dr. Kristina Yordanova

Institute of Computer Science, University of Rostock, 18051 Rostock, Germany
Website | E-Mail
Interests: activity and intention recognition; human behavior models; knowledge elicitation; natural language processing; automatic extraction of behavior models from textual sources

Special Issue Information

Dear Colleagues,

Ubiquitous systems are becoming an integral part of our everyday lives. Functionality and user experience often depend on accurate sensor-based activity recognition and interaction. Systems aiming to provide users with assistance or to monitor their behavior and condition rely heavily on sensors and the activities and interactions that they can recognize. Providing adequate activity recognition and interaction requires consideration of various interlocked aspects, such as sensors that are capable of capturing relevant behavior, rigorous methods to reason about sensor readings in the context of these behaviors, and effective approaches for assisting and interacting with the users. Each of these aspects is essential and can influence the quality and suitability of the provided service.

We solicit original submissions that contribute novel computer science methods, innovative software solutions, and compelling use cases on any of the following topics:

  • sensors, sensor infrastructures, and sensing technologies needed to detect user behaviors and to provide relevant interactions between systems and users;
  • data and model-driven methods for intelligent monitoring and user assistance that supports users in everyday settings;
  • novel applications and evaluation studies of methods for intelligent monitoring of everyday user behavior and user assistance using sensing technologies;
  • intelligent methods for synthesizing assistance and interaction strategies using sensing technologies.
Prof. Dr. Thomas Kirste
Prof. Dr. Bodo Urban
Dr. Kristina Yordanova
Guest Editors

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 papers will be 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. Informatics is an international peer-reviewed open access quarterly 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 350 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

  • Human activity recognition
  • Healthcare systems
  • Cognitive systems
  • Knowledge representation and reasoning
  • Expert Systems
  • NLP for intelligent systems
  • Ontologies for intelligent systems
  • Knowledge acquisition for intelligent systems
  • Assistive systems in the healthcare and manufacturing
  • Novel applications for assessing everyday behavior
  • Smart homes
  • Behavior monitoring and interpretation
  • Human performance measuring
  • Interaction techniques
  • Intelligent user interfaces
  • Input & output modalities
  • Wearable computing and wearable sensing
  • Context awareness
  • Data mining and machine learning for sensor-based intelligent systems
  • Signal reconstruction and interpolation
  • Innovative wearable sensing technologies
  • Machine learning techniques for interpretation of sensor data

Published Papers (5 papers)

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Research

Open AccessArticle Fitness Activity Recognition on Smartphones Using Doppler Measurements
Informatics 2018, 5(2), 24; https://doi.org/10.3390/informatics5020024
Received: 27 February 2018 / Revised: 29 April 2018 / Accepted: 2 May 2018 / Published: 4 May 2018
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Abstract
Quantified Self has seen an increased interest in recent years, with devices including smartwatches, smartphones, or other wearables that allow you to monitor your fitness level. This is often combined with mobile apps that use gamification aspects to motivate the user to perform
[...] Read more.
Quantified Self has seen an increased interest in recent years, with devices including smartwatches, smartphones, or other wearables that allow you to monitor your fitness level. This is often combined with mobile apps that use gamification aspects to motivate the user to perform fitness activities, or increase the amount of sports exercise. Thus far, most applications rely on accelerometers or gyroscopes that are integrated into the devices. They have to be worn on the body to track activities. In this work, we investigated the use of a speaker and a microphone that are integrated into a smartphone to track exercises performed close to it. We combined active sonar and Doppler signal analysis in the ultrasound spectrum that is not perceivable by humans. We wanted to measure the body weight exercises bicycles, toe touches, and squats, as these consist of challenging radial movements towards the measuring device. We have tested several classification methods, ranging from support vector machines to convolutional neural networks. We achieved an accuracy of 88% for bicycles, 97% for toe-touches and 91% for squats on our test set. Full article
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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Open AccessArticle An Internet of Things Based Multi-Level Privacy-Preserving Access Control for Smart Living
Informatics 2018, 5(2), 23; https://doi.org/10.3390/informatics5020023
Received: 25 January 2018 / Revised: 7 April 2018 / Accepted: 23 April 2018 / Published: 3 May 2018
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Abstract
The presence of the Internet of Things (IoT) in healthcare through the use of mobile medical applications and wearable devices allows patients to capture their healthcare data and enables healthcare professionals to be up-to-date with a patient’s status. Ambient Assisted Living (AAL), which
[...] Read more.
The presence of the Internet of Things (IoT) in healthcare through the use of mobile medical applications and wearable devices allows patients to capture their healthcare data and enables healthcare professionals to be up-to-date with a patient’s status. Ambient Assisted Living (AAL), which is considered as one of the major applications of IoT, is a home environment augmented with embedded ambient sensors to help improve an individual’s quality of life. This domain faces major challenges in providing safety and security when accessing sensitive health data. This paper presents an access control framework for AAL which considers multi-level access and privacy preservation. We focus on two major points: (1) how to use the data collected from ambient sensors and biometric sensors to perform the high-level task of activity recognition; and (2) how to secure the collected private healthcare data via effective access control. We achieve multi-level access control by extending Public Key Infrastructure (PKI) for secure authentication and utilizing Attribute-Based Access Control (ABAC) for authorization. The proposed access control system regulates access to healthcare data by defining policy attributes over healthcare professional groups and data classes classifications. We provide guidelines to classify the data classes and healthcare professional groups and describe security policies to control access to the data classes. Full article
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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Open AccessArticle Recognition of Physical Activities from a Single Arm-Worn Accelerometer: A Multiway Approach
Informatics 2018, 5(2), 20; https://doi.org/10.3390/informatics5020020
Received: 26 February 2018 / Revised: 29 March 2018 / Accepted: 9 April 2018 / Published: 16 April 2018
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Abstract
In current clinical practice, functional limitations due to chronic musculoskeletal diseases are still being assessed subjectively, e.g., using questionnaires and function scores. Performance-based methods, on the other hand, offer objective insights. Hence, they recently attracted more interest as an additional source of information.
[...] Read more.
In current clinical practice, functional limitations due to chronic musculoskeletal diseases are still being assessed subjectively, e.g., using questionnaires and function scores. Performance-based methods, on the other hand, offer objective insights. Hence, they recently attracted more interest as an additional source of information. This work offers a step towards the shift to performance-based methods by recognizing standardized activities from continuous readings using a single accelerometer mounted on a patient’s arm. The proposed procedure consists of two steps. Firstly, activities are segmented, including rejection of non-informative segments. Secondly, the segments are associated to predefined activities using a multiway pattern matching approach based on higher order discriminant analysis (HODA). The two steps are combined into a multi-layered framework. Experiments on data recorded from 39 patients with spondyloarthritis show results with a classification accuracy of 94.34% when perfect segmentation is assumed. Automatic segmentation has 89.32% overlap with this ideal scenario. However, combining both drops performance to 62.34% due to several badly-recognized subjects. Still, these results are shown to significantly outperform a more traditional pattern matching approach. Overall, the work indicates promising viability of the technique to automate recognition and, through future work, assessment, of functional capacity. Full article
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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Open AccessArticle Detecting Transitions in Manual Tasks from Wearables: An Unsupervised Labeling Approach
Informatics 2018, 5(2), 16; https://doi.org/10.3390/informatics5020016
Received: 28 February 2018 / Revised: 23 March 2018 / Accepted: 26 March 2018 / Published: 29 March 2018
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Abstract
Authoring protocols for manual tasks such as following recipes, manufacturing processes or laboratory experiments requires significant effort. This paper presents a system that estimates individual procedure transitions from the user’s physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or
[...] Read more.
Authoring protocols for manual tasks such as following recipes, manufacturing processes or laboratory experiments requires significant effort. This paper presents a system that estimates individual procedure transitions from the user’s physical movement and gestures recorded with inertial motion sensors. Combined with egocentric or external video recordings, this facilitates efficient review and annotation of video databases. We investigate different clustering algorithms on wearable inertial sensor data recorded on par with video data, to automatically create transition marks between task steps. The goal is to match these marks to the transitions given in a description of the workflow, thus creating navigation cues to browse video repositories of manual work. To evaluate the performance of unsupervised algorithms, the automatically-generated marks are compared to human expert-created labels on two publicly-available datasets. Additionally, we tested the approach on a novel dataset in a manufacturing lab environment, describing an existing sequential manufacturing process. The results from selected clustering methods are also compared to some supervised methods. Full article
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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Open AccessArticle A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment
Received: 13 December 2017 / Revised: 5 January 2018 / Accepted: 10 January 2018 / Published: 13 January 2018
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Abstract
Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities.
[...] Read more.
Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques. Full article
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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