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Sensor-Based Activity Recognition and Interaction

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 28882

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Special Issue Editors

Dr. Kristina Yordanova

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Guest Editor

Institute of Computer Science, University of Rostock, 18051 Rostock, Germany
Interests: activity and intention recognition; human behavior models; knowledge elicitation; natural language processing; automatic extraction of behavior models from textual sources
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Dr. Mario Aehnelt

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Guest Editor

Fraunhofer-Institut für Graphische Datenverarbeitung (IGD), 18059 Rostock, Germany
Interests: human activity recognition; vital data analysis; pattern recognition; artificial intelligence; mobile assistance

Dr. Gerald Bieber

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Guest Editor

Mr. Stefan Lüdtke

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Guest Editor

Institute of Visual & Analytic Computing, University of Rostock, 18051 Rostock, Germany
Interests: human activity recognition; human behavior models; lifted probabilistic inference; Bayesian filtering

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. The provision of adequate activity recognition and interaction requires consideration of various interlocked aspects, such as sensors that are capable of capturing relevant behavior, rigorous methods for reasoning based on sensor readings in the context of these behaviors, and effective approaches for assisting and interacting with users. Each of these aspects is essential, and can influence the quality and suitability of the provided service.

We are soliciting original submissions that contribute novel computer science methods, innovative software solutions, and cases of compelling use in 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.

Dr. Kristina Yordanova
Mr. Stefan Lüdtke
Dr. Mario Aehnelt
Dr. Gerald Bieber
Guest Editors

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Published Papers (5 papers)

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Research

17 pages, 3828 KiB

Open AccessArticle

Active Volume Control in Smart Phones Based on User Activity and Ambient Noise

by V. D. Ambeth Kumar, S. Malathi, Abhishek Kumar, Prakash M and Kalyana C. Veluvolu

Sensors 2020, 20(15), 4117; https://doi.org/10.3390/s20154117 - 24 Jul 2020

Cited by 36 | Viewed by 4495

Abstract

To communicate efficiently with a prospective user, auditory interfaces are employed in mobile communication devices. Diverse sounds in different volumes are used to alert the user in various devices such as mobile phones, modern laptops and domestic appliances. These alert noises behave erroneously in dynamic noise environments, leading to major annoyances to the user. In noisy environments, as sounds can be played quietly, this leads to the improper masked rendering of the necessary information. To overcome these issues, a multi-model sensing technique is developed as a smartphone application to achieve automatic volume control in a smart phone. Based on the ambient environment, the volume is automatically controlled such that it is maintained at an appropriate level for the user. By identifying the average noise level of the ambient environment from dynamic microphone and together with the activity recognition data obtained from the inertial sensors, the automatic volume control is achieved. Experiments are conducted with five different mobile devices at various noise-level environments and different user activity states. Results demonstrate the effectiveness of the proposed application for active volume control in dynamic environments. Full article

(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)

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21 pages, 5372 KiB

Open AccessArticle

The Role of Bedroom Privacy in Social Interaction among Elderly Residents in Nursing Homes: An Exploratory Case Study of Hong Kong

by Aria C. H. Yang, Newman Lau and Jeffrey C. F. Ho

Sensors 2020, 20(15), 4101; https://doi.org/10.3390/s20154101 - 23 Jul 2020

Cited by 9 | Viewed by 4359

Abstract

Privacy is often overlooked in Hong Kong nursing homes with the majority of elderly residents living in shared bedrooms of three to five people. Only a few studies have used Bluetooth low energy indoor positioning systems to explore the relationship between privacy and social interaction among elderly residents. The study investigates the social behavioural patterns of elderly residents living in three-bed, four-bed, and five-bed rooms in a nursing home. Location data of 50 residents were used for the identification of mobility and social interaction patterns in relation to different degrees of privacy and tested for statistical significance. Privacy is found to have a weak negative correlation with mobility patterns and social behaviour, implying that the more privacy there is, the less mobility and more formal interaction is found. Residents who had more privacy did not spend more time in social space. Residents living in bedrooms that opened directly onto social space had higher social withdrawal tendencies, indicating the importance of transitional spaces between private and public areas. Friends’ rooms were used extensively by residents who had little privacy, however, the concept of friends’ rooms have rarely been discussed in nursing homes. There is evidence supporting the importance of privacy for social interaction. Future study directions include considering how other design factors, such as configuration and social space diversity, work with privacy to influence social interaction. Full article

(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)

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42 pages, 29328 KiB

Open AccessArticle

LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes

by Friedrich Niemann, Christopher Reining, Fernando Moya Rueda, Nilah Ravi Nair, Janine Anika Steffens, Gernot A. Fink and Michael ten Hompel

Sensors 2020, 20(15), 4083; https://doi.org/10.3390/s20154083 - 22 Jul 2020

Cited by 46 | Viewed by 10395

Abstract

Optimizations in logistics require recognition and analysis of human activities. The potential of sensor-based human activity recognition (HAR) in logistics is not yet well explored. Despite a significant increase in HAR datasets in the past twenty years, no available dataset depicts activities in logistics. This contribution presents the first freely accessible logistics-dataset. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recreated. Fourteen subjects were recorded individually when performing warehousing activities using Optical marker-based Motion Capture (OMoCap), inertial measurement units (IMUs), and an RGB camera. A total of 758 min of recordings were labeled by 12 annotators in 474 person-h. All the given data have been labeled and categorized into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The dataset is deployed for solving HAR using deep networks. Full article

(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)

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22 pages, 1068 KiB

Open AccessArticle

Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance

by Sebastian Scheurer, Salvatore Tedesco, Brendan O’Flynn and Kenneth N. Brown

Sensors 2020, 20(13), 3647; https://doi.org/10.3390/s20133647 - 29 Jun 2020

Cited by 6 | Viewed by 3112

Abstract

The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms’ subject-dependent and subject-independent performances across eight datasets using three different personalisation–generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted,

κ

-weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and

κ

-weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance. Full article

(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)

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14 pages, 43745 KiB

Open AccessArticle

Sensory Interactive Table (SIT)—Development of a Measurement Instrument to Support Healthy Eating in a Social Dining Setting

by Juliet A. M. Haarman, Roelof A. J. de Vries, Emiel C. Harmsen, Hermie J. Hermens and Dirk K. J. Heylen

Sensors 2020, 20(9), 2636; https://doi.org/10.3390/s20092636 - 5 May 2020

Cited by 9 | Viewed by 5640

Abstract

This paper presents the Sensory Interactive Table (SIT): an instrumented, interactive dining table. Through the use of load cells and LEDs that are embedded in the table surface, SIT allows us to study: (1) the eating behaviors of people in a social setting, (2) the social interactions around the eating behaviors of people in a social setting, and (3) the continuous cycle of feedback through LEDs on people’s eating behavior and their response to this feedback in real time, to ultimately create an effective dietary support system. This paper presents the hard- and software specifications of the system, and it shows the potential of the system to capture mass-related dimensions in real time and with high accuracy and spatial resolution. Full article

(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)

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