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Informatics, Volume 5, Issue 2 (June 2018)

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Open AccessArticle Towards Clustering of Mobile and Smartwatch Accelerometer Data for Physical Activity Recognition
Informatics 2018, 5(2), 29; https://doi.org/10.3390/informatics5020029
Received: 1 March 2018 / Revised: 7 June 2018 / Accepted: 8 June 2018 / Published: 12 June 2018
Cited by 2 | Viewed by 1365 | PDF Full-text (7091 KB) | HTML Full-text | XML Full-text
Abstract
Mobile and wearable devices now have a greater capability of sensing human activity ubiquitously and unobtrusively through advancements in miniaturization and sensing abilities. However, outstanding issues remain around the energy restrictions of these devices when processing large sets of data. This paper presents
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Mobile and wearable devices now have a greater capability of sensing human activity ubiquitously and unobtrusively through advancements in miniaturization and sensing abilities. However, outstanding issues remain around the energy restrictions of these devices when processing large sets of data. This paper presents our approach that uses feature selection to refine the clustering of accelerometer data to detect physical activity. This also has a positive effect on the computational burden that is associated with processing large sets of data, as energy efficiency and resource use is decreased because less data is processed by the clustering algorithms. Raw accelerometer data, obtained from smartphones and smartwatches, have been preprocessed to extract both time and frequency domain features. Principle component analysis feature selection (PCAFS) and correlation feature selection (CFS) have been used to remove redundant features. The reduced feature sets have then been evaluated against three widely used clustering algorithms, including hierarchical clustering analysis (HCA), k-means, and density-based spatial clustering of applications with noise (DBSCAN). Using the reduced feature sets resulted in improved separability, reduced uncertainty, and improved efficiency compared with the baseline, which utilized all features. Overall, the CFS approach in conjunction with HCA produced higher Dunn Index results of 9.7001 for the phone and 5.1438 for the watch features, which is an improvement over the baseline. The results of this comparative study of feature selection and clustering, with the specific algorithms used, has not been performed previously and provides an optimistic and usable approach to recognize activities using either a smartphone or smartwatch. Full article
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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Open AccessArticle Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove
Informatics 2018, 5(2), 28; https://doi.org/10.3390/informatics5020028
Received: 28 February 2018 / Revised: 4 June 2018 / Accepted: 6 June 2018 / Published: 11 June 2018
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Abstract
This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user’s hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition
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This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user’s hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition of various, possibly complex hand gestures, however, introduces unintentional overhead to the system. Consequently, we present a data glove prototype comprising a glove-embedded gesture classifier utilizing data from Inertial Measurement Units (IMUs) in the fingertips. In an extensive set of experiments with 57 participants, our system was tested with 22 hand gestures, all taken from the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, using complementary filter with a gyroscope-to-accelerometer ratio of 93%. Our approach has also been compared to the local fusion algorithm on an IMU motion sensor, showing faster settling times and less delays after gesture changes. Real-time performance of the recognition is shown to occur within 63 milliseconds, allowing fluent use of the gestures via Bluetooth-connected systems. Full article
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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Open AccessArticle A Comprehensive Study of Activity Recognition Using Accelerometers
Informatics 2018, 5(2), 27; https://doi.org/10.3390/informatics5020027
Received: 16 March 2018 / Revised: 22 May 2018 / Accepted: 25 May 2018 / Published: 30 May 2018
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Abstract
This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are
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This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions. Full article
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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Open AccessArticle Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors
Informatics 2018, 5(2), 26; https://doi.org/10.3390/informatics5020026
Received: 7 March 2018 / Revised: 14 May 2018 / Accepted: 22 May 2018 / Published: 25 May 2018
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Abstract
Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes
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Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs). Full article
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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Open AccessArticle Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems
Informatics 2018, 5(2), 25; https://doi.org/10.3390/informatics5020025
Received: 4 December 2017 / Revised: 29 April 2018 / Accepted: 7 May 2018 / Published: 9 May 2018
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Abstract
Recommender systems are powerful online tools that help to overcome problems of information overload. They make personalized recommendations to online users using various data mining and filtering techniques. However, most of the existing recommender systems use a single rating to represent the preference
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Recommender systems are powerful online tools that help to overcome problems of information overload. They make personalized recommendations to online users using various data mining and filtering techniques. However, most of the existing recommender systems use a single rating to represent the preference of user on an item. These techniques have several limitations as the preference of the user towards items may depend on several attributes of the items. Multi-criteria recommender systems extend the single rating recommendation techniques to incorporate multiple criteria ratings for improving recommendation accuracy. However, modeling the criteria ratings in multi-criteria recommender systems to determine the overall preferences of users has been considered as one of the major challenges in multi-criteria recommender systems. In other words, how to additionally take the multi-criteria rating information into account during the recommendation process is one of the problems of multi-criteria recommender systems. This article presents a methodological framework that trains artificial neural networks with particle swarm optimization algorithms and uses the neural networks for integrating the multi-criteria rating information and determining the preferences of users. The proposed neural network-based multi-criteria recommender system is integrated with k-nearest neighborhood collaborative filtering for predicting unknown criteria ratings. The proposed approach has been tested with a multi-criteria dataset for recommending movies to users. The empirical results of the study show that the proposed model has a higher prediction accuracy than the corresponding traditional recommendation technique and other multi-criteria recommender systems. Full article
(This article belongs to the Special Issue Advances in Recommender Systems)
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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
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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
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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 Building Realistic Mobility Models for Mobile Ad Hoc Networks
Informatics 2018, 5(2), 22; https://doi.org/10.3390/informatics5020022
Received: 8 February 2018 / Revised: 17 April 2018 / Accepted: 23 April 2018 / Published: 30 April 2018
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Abstract
A mobile ad hoc network (MANET) is a self-configuring wireless network in which each node could act as a router, as well as a data source or sink. Its application areas include battlefields and vehicular and disaster areas. Many techniques applied to infrastructure-based
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A mobile ad hoc network (MANET) is a self-configuring wireless network in which each node could act as a router, as well as a data source or sink. Its application areas include battlefields and vehicular and disaster areas. Many techniques applied to infrastructure-based networks are less effective in MANETs, with routing being a particular challenge. This paper presents a rigorous study into simulation techniques for evaluating routing solutions for MANETs with the aim of producing more realistic simulation models and thereby, more accurate protocol evaluations. MANET simulations require models that reflect the world in which the MANET is to operate. Much of the published research uses movement models, such as the random waypoint (RWP) model, with arbitrary world sizes and node counts. This paper presents a technique for developing more realistic simulation models to test and evaluate MANET protocols. The technique is animation, which is applied to a realistic scenario to produce a model that accurately reflects the size and shape of the world, node count, movement patterns, and time period over which the MANET may operate. The animation technique has been used to develop a battlefield model based on established military tactics. Trace data has been used to build a model of maritime movements in the Irish Sea. Similar world models have been built using the random waypoint movement model for comparison. All models have been built using the ns-2 simulator. These models have been used to compare the performance of three routing protocols: dynamic source routing (DSR), destination-sequenced distance-vector routing (DSDV), and ad hoc n-demand distance vector routing (AODV). The findings reveal that protocol performance is dependent on the model used. In particular, it is shown that RWP models do not reflect the performance of these protocols under realistic circumstances, and protocol selection is subject to the scenario to which it is applied. To conclude, it is possible to develop a range of techniques for modelling scenarios applicable to MANETs, and these simulation models could be utilised for the evaluation of routing protocols. Full article
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Open AccessArticle Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems
Informatics 2018, 5(2), 21; https://doi.org/10.3390/informatics5020021
Received: 9 March 2018 / Revised: 21 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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Abstract
One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users’ past behavior
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One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users’ past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1) we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2) we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user’s interests, thus improving prediction quality. Full article
(This article belongs to the Special Issue Advances in Recommender Systems)
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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.
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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 AccessEditorial Quality Management in Big Data
Informatics 2018, 5(2), 19; https://doi.org/10.3390/informatics5020019
Received: 11 April 2018 / Revised: 11 April 2018 / Accepted: 12 April 2018 / Published: 16 April 2018
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Abstract
Due to the importance of quality issues in Big Data, Big Data quality management has attracted significant research attention on how to measure, improve and manage the quality for Big Data. This special issue in the Journal of Informatics thus tends to address
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Due to the importance of quality issues in Big Data, Big Data quality management has attracted significant research attention on how to measure, improve and manage the quality for Big Data. This special issue in the Journal of Informatics thus tends to address the quality problems in Big Data as well as promote further research for Big Data quality. Our editorial describes the state-of-the-art research challenges in the Big Data quality research, and highlights the contributions of each paper accepted in this special issue. Full article
(This article belongs to the Special Issue Quality Management in Big Data)
Open AccessArticle Data Provenance for Agent-Based Models in a Distributed Memory
Informatics 2018, 5(2), 18; https://doi.org/10.3390/informatics5020018
Received: 12 December 2017 / Revised: 4 April 2018 / Accepted: 4 April 2018 / Published: 9 April 2018
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Abstract
Agent-Based Models (ABMs) assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The
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Agent-Based Models (ABMs) assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The Multi-Agent Spatial Simulation (MASS) library provides a framework for simulating ABMs at fine granularity, where agents and spatial data are shared application resources in a distributed memory. We introduce a novel approach to capture ABM provenance in a distributed memory, called ProvMASS. We evaluate our technique with traditional data provenance queries and performance measures. Our results indicate that a configurable approach can capture provenance that explains coordination of distributed shared resources, simulation logic, and agent behavior while limiting performance overhead. We also show the ability to support practical analyses (e.g., agent tracking) and storage requirements for different capture configurations. Full article
(This article belongs to the Special Issue Using Computational Provenance)
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Open AccessArticle A Recommender System for Programming Online Judges Using Fuzzy Information Modeling
Informatics 2018, 5(2), 17; https://doi.org/10.3390/informatics5020017
Received: 19 February 2018 / Revised: 25 March 2018 / Accepted: 29 March 2018 / Published: 3 April 2018
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Abstract
Programming online judges (POJs) are an emerging application scenario in e-learning recommendation areas. Specifically, they are e-learning tools usually used in programming practices for the automatic evaluation of source code developed by students when they are solving programming problems. Usually, they contain a
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Programming online judges (POJs) are an emerging application scenario in e-learning recommendation areas. Specifically, they are e-learning tools usually used in programming practices for the automatic evaluation of source code developed by students when they are solving programming problems. Usually, they contain a large collection of such problems, to be solved by students at their own personalized pace. The more problems in the POJ the harder the selection of the right problem to solve according to previous users performance, causing information overload and a widespread discouragement. This paper presents a recommendation framework to mitigate this issue by suggesting problems to solve in programming online judges, through the use of fuzzy tools which manage the uncertainty related to this scenario. The evaluation of the proposal uses real data obtained from a programming online judge, and shows that the new approach improves previous recommendation strategies which do not consider uncertainty management in the programming online judge scenarios. Specifically, the best results were obtained for short recommendation lists. Full article
(This article belongs to the Special Issue Advances in Recommender Systems)
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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
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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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