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Special Issue "Inertial Sensors for Activity Recognition and Classification"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (15 June 2019)

Special Issue Editors

Guest Editor
Prof. Angelo Maria Sabatini

The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy
E-Mail
Interests: wearable sensor systems for human motion capture; methods of human performance assessment; magneto-inertial measurement units; methods of signal processing for wearable sensors; sensor fusion
Guest Editor
Dr. Andrea Mannini

The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy
Website | E-Mail
Interests: wearable sensors; machine learning; activity recognition; inertial sensors; movement analysis; gait parameters estimation; automatic early detection of gait alterations; sports bioengineering; mobile health

Special Issue Information

Dear Colleagues,

Inertial sensors and inertial measurement units are witnessing increasing interest among practitioners in several related technical fields that require the capability of analyzing human movement patterns: biomechanics, clinical biomechanics, sport, physical medicine and rehabilitation, and telehealth, to name just a few.

The interest is also due to the widespread availability of personal devices with embedded sensors (i.e., smartphones, smartwatches) or, more generally, of wearable sensor systems, which both offer unique opportunities in terms of acquisition, logging, processing, and transmitting movement data.

A common feature to several applications in the mentioned fields is the capability of performing tasks of automatic recognition (i.e., understanding the activity being performed) and classification (i.e., understanding how the recognized activity is performed), especially in ecological conditions. These conditions recur when the activities of interest are carried out without any undue constraint affecting their execution by persons who can be either healthy or affected by movement disorders.

Although significant improvements have been made, especially in the past few years, a number of issues still remain to be solved, either technologically or perhaps more urgently, methodologically.

This Special Issue aims to highlight advances in the development and validation of computational methods that specifically address the problem of automatic recognition and classification of human movement patterns using inertial sensors, without restrictions for the prospective applications. Topics include, but are not limited, to:

  • Machine learning methods for automatic recognition and classification;
  • Novel methodologies for personalized/adaptive classifier training and validation in ecological conditions;
  • Sensor fusion and multi-sensor integration methods for the extraction of relevant signal features;
  • Detection of specific events or conditions, such as gestures, falls, gait disturbances, etc.;
  • Signal processing for biofeedback and actuation.

Prof. Angelo Maria Sabatini
Dr. Andrea Mannini
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. Sensors is an international peer-reviewed open access semimonthly 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 1800 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

  • inertial sensors
  • accelerometers and gyroscopes
  • motion capture
  • machine learning
  • sensor fusion

Published Papers (4 papers)

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Research

Open AccessArticle
Real-Time Drink Trigger Detection in Free-living Conditions Using Inertial Sensors
Sensors 2019, 19(9), 2145; https://doi.org/10.3390/s19092145
Received: 21 March 2019 / Revised: 30 April 2019 / Accepted: 1 May 2019 / Published: 9 May 2019
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Abstract
Despite the importance of maintaining an adequate hydration status, water intake is frequently neglected due to the fast pace of people’s lives. For the elderly, poor water intake can be even more concerning, not only due to the damaging impact of dehydration, but [...] Read more.
Despite the importance of maintaining an adequate hydration status, water intake is frequently neglected due to the fast pace of people’s lives. For the elderly, poor water intake can be even more concerning, not only due to the damaging impact of dehydration, but also since seniors’ hydration regulation mechanisms tend to be less efficient. This work focuses on the recognition of the pre-drinking hand-to-mouth movement (a drink trigger) with two main objectives: predict the occurrence of drinking events in real-time and free-living conditions, and assess the potential of using this method to trigger an external component for estimating the amount of fluid intake. This shall contribute towards the efficiency of more robust multimodal approaches addressing the problem of water intake monitoring. The system, based on a single inertial measurement unit placed on the forearm, is unobtrusive, user-independent, and lightweight enough for real-time mobile processing. Drinking events outside meal periods were detected with an F-score of 97% in an offline validation with data from 12 users, and 85% in a real-time free-living validation with five other subjects, using a random forest classifier. Our results also reveal that the algorithm first detects the hand-to-mouth movement 0.70 s before the occurrence of the actual sip of the drink, proving that this approach can have further applications and enable more robust and complete fluid intake monitoring solutions. Full article
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
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Open AccessArticle
On Placement, Location and Orientation of Wrist-Worn Tri-Axial Accelerometers during Free-Living Measurements
Sensors 2019, 19(9), 2095; https://doi.org/10.3390/s19092095
Received: 12 March 2019 / Revised: 28 April 2019 / Accepted: 1 May 2019 / Published: 6 May 2019
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Abstract
Wearable accelerometers have recently become a standalone tool for the objective assessment of physical activity (PA). In free-living studies, accelerometers are placed by protocol on a pre-defined body location (e.g., non-dominant wrist). However, the protocol is not always followed, e.g., the sensor can [...] Read more.
Wearable accelerometers have recently become a standalone tool for the objective assessment of physical activity (PA). In free-living studies, accelerometers are placed by protocol on a pre-defined body location (e.g., non-dominant wrist). However, the protocol is not always followed, e.g., the sensor can be moved between wrists or reattached in a different orientation. Such protocol violations often result in PA miscalculation. We propose an approach, PLOE (“Placement, Location and Orientation Evaluation method”), to determine the sensor position using statistical features from the raw accelerometer measurements. We compare the estimated position with the study protocol and identify discrepancies. We apply PLOE to the measurements collected from 45 older adults who wore ActiGraph GT3X+ accelerometers on the left and right wrist for seven days. We found that 15.6% of participants who wore accelerometers violated the protocol for one or more days. The sensors were worn on the wrong hand during 6.9% of the days of simultaneous wearing of devices. During the periods of discrepancies, the daily PA was miscalculated by more than 20%. Our findings show that correct placement of the device has a significant effect on the PA estimates. These results demonstrate a need for the evaluation of sensor position. Full article
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
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Open AccessArticle
Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables
Sensors 2019, 19(8), 1820; https://doi.org/10.3390/s19081820
Received: 2 March 2019 / Revised: 1 April 2019 / Accepted: 12 April 2019 / Published: 16 April 2019
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Abstract
Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. The effort required to obtain and annotate such datasets is massive, therefore we propose a [...] Read more.
Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. The effort required to obtain and annotate such datasets is massive, therefore we propose a smart annotation pipeline which reduces the number of events needing manual adjustment to 14%. For scenarios dominated by walking, this annotation effort is as low as 8%. The pipeline consists of three smart annotation approaches, namely edge detection of the pressure data, local cyclicity estimation, and iteratively trained hierarchical hidden Markov models. Using this pipeline, we have collected and labeled a dataset with over 150,000 labeled cycles, each with 2 phases, from 80 subjects, which we have made publicly available. The dataset consists of 12 different task-driven activities, 10 of which are cyclic. These activities include not only straight and steady-state motions, but also transitions, different ranges of bouts, and changing directions. Each participant wore 5 synchronized inertial measurement units (IMUs) on the wrists, shoes, and in a pocket, as well as pressure insoles and video. We believe that this dataset and smart annotation pipeline are a good basis for creating a benchmark dataset for validation of other semi- and unsupervised algorithms. Full article
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
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Open AccessArticle
Recognition and Repetition Counting for Complex Physical Exercises with Deep Learning
Sensors 2019, 19(3), 714; https://doi.org/10.3390/s19030714
Received: 21 December 2018 / Revised: 1 February 2019 / Accepted: 5 February 2019 / Published: 10 February 2019
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Abstract
Activity recognition using off-the-shelf smartwatches is an important problem in human activity recognition. In this paper, we present an end-to-end deep learning approach, able to provide probability distributions over activities from raw sensor data. We apply our methods to 10 complex full-body exercises [...] Read more.
Activity recognition using off-the-shelf smartwatches is an important problem in human activity recognition. In this paper, we present an end-to-end deep learning approach, able to provide probability distributions over activities from raw sensor data. We apply our methods to 10 complex full-body exercises typical in CrossFit, and achieve a classification accuracy of 99.96%. We additionally show that the same neural network used for exercise recognition can also be used in repetition counting. To the best of our knowledge, our approach to repetition counting is novel and performs well, counting correctly within an error of ±1 repetitions in 91% of the performed sets. Full article
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
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