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Special Issue "Body Worn Behavior Sensing"

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

Deadline for manuscript submissions: closed (30 September 2016)

Special Issue Editor

Guest Editor
Prof. Dr. Kamiar Aminian

Laboratory of Movement Analysis and Measurement, Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne (EPFL), STI-IBI2-LMAM, Station 9, CH-1015 Lausanne, Switzerland
Website | E-Mail
Interests: Inertial sensors; algorithms for wearable systems; motion capture and tracking; activity monitoring; biomechanical instrumentation; sport performance measuring

Special Issue Information

Dear Colleagues,

Human daily-life behavior is influenced by environment, emotion, cognition state, and personal traits of subjects and includes voluntary actions, such as physical activity, face-to-face interaction, call, lifestyle choices, but also spontaneous activity (fidgeting, maintaining posture) and physiological activity. This Special Issue addresses wearable measuring systems where sensors, such as GPS, inertial sensors, gaze trackers, skin conductivity sensors, thermal sensing, microphone, camera, electrodes, and other body worn sensors, are used for behavior sensing.

We invite original contribution including sensors used for behavior monitoring, validity and reliability of sensing modality, signal processing, and modeling of behavior data. This special issue is also interested by new algorithms based on body worn systems that have been applied to study behavior in health and disease, through monitoring of sleep, physical activity, emotion, cognition, wellness, sedentarity, and life-style.

Prof. Dr. Kamiar Aminian
Guest Editor

Manuscript Submission Information

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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

  • mobile health
  • body worn sensors
  • sensor fusion
  • behavior monitoring
  • sleep monitoring
  • Behavior modelling
  • Emotion recognition
  • Cognitive assessment

Published Papers (14 papers)

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Open AccessArticle Mobile Health Applications to Promote Active and Healthy Ageing
Sensors 2017, 17(3), 622; doi:10.3390/s17030622
Received: 3 November 2016 / Revised: 14 March 2017 / Accepted: 15 March 2017 / Published: 18 March 2017
Cited by 4 | PDF Full-text (1271 KB) | HTML Full-text | XML Full-text
Abstract
The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet
[...] Read more.
The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet the societal challenge of changing demography, mobile health solutions are warranted that support older adults to stay healthy and active and that can prevent or delay functional decline. This paper reviews the literature on mobile technology, in particular wearable technology, such as smartphones, smartwatches, and wristbands, presenting new ideas on how this technology can be used to encourage an active lifestyle, and discusses the way forward in order further to advance development and practice in the field of mobile technology for active, healthy ageing. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology—The ADAPT Study Data-Set
Sensors 2017, 17(3), 559; doi:10.3390/s17030559
Received: 27 January 2017 / Revised: 24 February 2017 / Accepted: 8 March 2017 / Published: 10 March 2017
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Abstract
Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised
[...] Read more.
Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects’ movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects’ movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen’s Kappa, corrected kappa, Krippendorff’s alpha and Fleiss’ kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition
Sensors 2017, 17(2), 319; doi:10.3390/s17020319
Received: 21 December 2016 / Revised: 1 February 2017 / Accepted: 6 February 2017 / Published: 8 February 2017
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Abstract
Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement
[...] Read more.
Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture
Sensors 2017, 17(1), 75; doi:10.3390/s17010075
Received: 29 September 2016 / Revised: 21 December 2016 / Accepted: 28 December 2016 / Published: 31 December 2016
Cited by 4 | PDF Full-text (6215 KB) | HTML Full-text | XML Full-text
Abstract
Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in
[...] Read more.
Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Measurement and Geometric Modelling of Human Spine Posture for Medical Rehabilitation Purposes Using a Wearable Monitoring System Based on Inertial Sensors
Sensors 2017, 17(1), 0003; doi:10.3390/s17010003
Received: 27 September 2016 / Revised: 28 November 2016 / Accepted: 11 December 2016 / Published: 22 December 2016
Cited by 2 | PDF Full-text (14429 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a mathematical model that can be used to virtually reconstruct the posture of the human spine. By using orientation angles from a wearable monitoring system based on inertial sensors, the model calculates and represents the curvature of the spine. Several
[...] Read more.
This paper presents a mathematical model that can be used to virtually reconstruct the posture of the human spine. By using orientation angles from a wearable monitoring system based on inertial sensors, the model calculates and represents the curvature of the spine. Several hypotheses are taken into consideration to increase the model precision. An estimation of the postures that can be calculated is also presented. A non-invasive solution to identify the human back shape can help reducing the time needed for medical rehabilitation sessions. Moreover, it prevents future problems caused by poor posture. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?
Sensors 2016, 16(12), 2138; doi:10.3390/s16122138
Received: 30 September 2016 / Revised: 4 December 2016 / Accepted: 8 December 2016 / Published: 15 December 2016
Cited by 2 | PDF Full-text (606 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high-quality motion capture data is available in large quantities,
[...] Read more.
Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high-quality motion capture data is available in large quantities, this creates possibilities to further simplify hardware setups, by use of data-driven methods to decrease the number of body-worn sensors. In this work, we contribute to this field by analyzing the capabilities of using either artificial neural networks (eager learning) or nearest neighbor search (lazy learning) for such a problem. Sparse orientation features, resulting from sensor fusion of only five inertial measurement units with magnetometers, are mapped to full-body poses. Both eager and lazy learning algorithms are shown to be capable of constructing this mapping. The full-body output poses are visually plausible with an average joint position error of approximately 7 cm, and average joint angle error of 7 . Additionally, the effects of magnetic disturbances typical in orientation tracking on the estimation of full-body poses was also investigated, where nearest neighbor search showed better performance for such disturbances. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Assessing Motor Fluctuations in Parkinson’s Disease Patients Based on a Single Inertial Sensor
Sensors 2016, 16(12), 2132; doi:10.3390/s16122132
Received: 23 September 2016 / Revised: 27 November 2016 / Accepted: 10 December 2016 / Published: 15 December 2016
Cited by 3 | PDF Full-text (2865 KB) | HTML Full-text | XML Full-text
Abstract
Altered movement control is typically the first noticeable symptom manifested by Parkinson’s disease (PD) patients. Once under treatment, the effect of the medication is very patent and patients often recover correct movement control over several hours. Nonetheless, as the disease advances, patients present
[...] Read more.
Altered movement control is typically the first noticeable symptom manifested by Parkinson’s disease (PD) patients. Once under treatment, the effect of the medication is very patent and patients often recover correct movement control over several hours. Nonetheless, as the disease advances, patients present motor complications. Obtaining precise information on the long-term evolution of these motor complications and their short-term fluctuations is crucial to provide optimal therapy to PD patients and to properly measure the outcome of clinical trials. This paper presents an algorithm based on the accelerometer signals provided by a waist sensor that has been validated in the automatic assessment of patient’s motor fluctuations (ON and OFF motor states) during their activities of daily living. A total of 15 patients have participated in the experiments in ambulatory conditions during 1 to 3 days. The state recognised by the algorithm and the motor state annotated by patients in standard diaries are contrasted. Results show that the average specificity and sensitivity are higher than 90%, while their values are higher than 80% of all patients, thereby showing that PD motor status is able to be monitored through a single sensor during daily life of patients in a precise and objective way. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study
Sensors 2016, 16(12), 2105; doi:10.3390/s16122105
Received: 30 September 2016 / Revised: 12 November 2016 / Accepted: 5 December 2016 / Published: 11 December 2016
Cited by 1 | PDF Full-text (2594 KB) | HTML Full-text | XML Full-text
Abstract
The popularity of using wearable inertial sensors for physical activity classification has dramatically increased in the last decade due to their versatility, low form factor, and low power requirements. Consequently, various systems have been developed to automatically classify daily life activities. However, the
[...] Read more.
The popularity of using wearable inertial sensors for physical activity classification has dramatically increased in the last decade due to their versatility, low form factor, and low power requirements. Consequently, various systems have been developed to automatically classify daily life activities. However, the scope and implementation of such systems is limited to laboratory-based investigations. Furthermore, these systems are not directly comparable, due to the large diversity in their design (e.g., number of sensors, placement of sensors, data collection environments, data processing techniques, features set, classifiers, cross-validation methods). Hence, the aim of this study is to propose a fair and unbiased benchmark for the field-based validation of three existing systems, highlighting the gap between laboratory and real-life conditions. For this purpose, three representative state-of-the-art systems are chosen and implemented to classify the physical activities of twenty older subjects (76.4 ± 5.6 years). The performance in classifying four basic activities of daily life (sitting, standing, walking, and lying) is analyzed in controlled and free living conditions. To observe the performance of laboratory-based systems in field-based conditions, we trained the activity classification systems using data recorded in a laboratory environment and tested them in real-life conditions in the field. The findings show that the performance of all systems trained with data in the laboratory setting highly deteriorates when tested in real-life conditions, thus highlighting the need to train and test the classification systems in the real-life setting. Moreover, we tested the sensitivity of chosen systems to window size (from 1 s to 10 s) suggesting that overall accuracy decreases with increasing window size. Finally, to evaluate the impact of the number of sensors on the performance, chosen systems are modified considering only the sensing unit worn at the lower back. The results, similarly to the multi-sensor setup, indicate substantial degradation of the performance when laboratory-trained systems are tested in the real-life setting. This degradation is higher than in the multi-sensor setup. Still, the performance provided by the single-sensor approach, when trained and tested with real data, can be acceptable (with an accuracy above 80%). Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform
Sensors 2016, 16(12), 2048; doi:10.3390/s16122048
Received: 7 September 2016 / Revised: 17 November 2016 / Accepted: 21 November 2016 / Published: 2 December 2016
Cited by 5 | PDF Full-text (3131 KB) | HTML Full-text | XML Full-text
Abstract
Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform
[...] Read more.
Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Flexible Graphene Electrodes for Prolonged Dynamic ECG Monitoring
Sensors 2016, 16(11), 1833; doi:10.3390/s16111833
Received: 12 July 2016 / Revised: 15 October 2016 / Accepted: 28 October 2016 / Published: 1 November 2016
Cited by 4 | PDF Full-text (3398 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper describes the development of a graphene-based dry flexible electrocardiography (ECG) electrode and a portable wireless ECG measurement system. First, graphene films on polyethylene terephthalate (PET) substrates and graphene paper were used to construct the ECG electrode. Then, a graphene textile was
[...] Read more.
This paper describes the development of a graphene-based dry flexible electrocardiography (ECG) electrode and a portable wireless ECG measurement system. First, graphene films on polyethylene terephthalate (PET) substrates and graphene paper were used to construct the ECG electrode. Then, a graphene textile was synthesized for the fabrication of a wearable ECG monitoring system. The structure and the electrical properties of the graphene electrodes were evaluated using Raman spectroscopy, scanning electron microscopy (SEM), and alternating current impedance spectroscopy. ECG signals were then collected from healthy subjects using the developed graphene electrode and portable measurement system. The results show that the graphene electrode was able to acquire the typical characteristics and features of human ECG signals with a high signal-to-noise (SNR) ratio in different states of motion. A week-long continuous wearability test showed no degradation in the ECG signal quality over time. The graphene-based flexible electrode demonstrates comfortability, good biocompatibility, and high electrophysiological detection sensitivity. The graphene electrode also combines the potential for use in long-term wearable dynamic cardiac activity monitoring systems with convenience and comfort for use in home health care of elderly and high-risk adults. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Hierarchical Activity Recognition Using Smart Watches and RGB-Depth Cameras
Sensors 2016, 16(10), 1713; doi:10.3390/s16101713
Received: 12 July 2016 / Revised: 25 September 2016 / Accepted: 7 October 2016 / Published: 15 October 2016
PDF Full-text (4729 KB) | HTML Full-text | XML Full-text
Abstract
Human activity recognition is important for healthcare and lifestyle evaluation. In this paper, a novel method for activity recognition by jointly considering motion sensor data recorded by wearable smart watches and image data captured by RGB-Depth (RGB-D) cameras is presented. A normalized cross
[...] Read more.
Human activity recognition is important for healthcare and lifestyle evaluation. In this paper, a novel method for activity recognition by jointly considering motion sensor data recorded by wearable smart watches and image data captured by RGB-Depth (RGB-D) cameras is presented. A normalized cross correlation based mapping method is implemented to establish association between motion sensor data with corresponding image data from the same person in multi-person situations. Further, to improve the performance and accuracy of recognition, a hierarchical structure embedded with an automatic group selection method is proposed. Through this method, if the number of activities to be classified is changed, the structure will be changed correspondingly without interaction. Our comparative experiments against the single data source and single layer methods have shown that our method is more accurate and robust. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?
Sensors 2016, 16(6), 800; doi:10.3390/s16060800
Received: 5 February 2016 / Revised: 19 May 2016 / Accepted: 23 May 2016 / Published: 1 June 2016
Cited by 15 | PDF Full-text (3172 KB) | HTML Full-text | XML Full-text
Abstract
Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in
[...] Read more.
Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject’s daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data). Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
Open AccessArticle Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors
Sensors 2016, 16(4), 426; doi:10.3390/s16040426
Received: 22 January 2016 / Revised: 23 February 2016 / Accepted: 17 March 2016 / Published: 24 March 2016
Cited by 22 | PDF Full-text (2213 KB) | HTML Full-text | XML Full-text
Abstract
The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve
[...] Read more.
The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)

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Open AccessLetter Patterns-of-Life Aided Authentication
Sensors 2016, 16(10), 1574; doi:10.3390/s16101574
Received: 12 June 2016 / Revised: 13 September 2016 / Accepted: 20 September 2016 / Published: 23 September 2016
PDF Full-text (1644 KB) | HTML Full-text | XML Full-text
Abstract
Wireless Body Area Network (WBAN) applications have grown immensely in the past few years. However, security and privacy of the user are two major obstacles in their development. The complex and very sensitive nature of the body-mounted sensors means the traditional network layer
[...] Read more.
Wireless Body Area Network (WBAN) applications have grown immensely in the past few years. However, security and privacy of the user are two major obstacles in their development. The complex and very sensitive nature of the body-mounted sensors means the traditional network layer security arrangements are not sufficient to employ their full potential, and novel solutions are necessary. In contrast, security methods based on physical layers tend to be more suitable and have simple requirements. The problem of initial trust needs to be addressed as a prelude to the physical layer security key arrangement. This paper proposes a patterns-of-life aided authentication model to solve this issue. The model employs the wireless channel fingerprint created by the user’s behavior characterization. The performance of the proposed model is established through experimental measurements at 2.45 GHz. Experimental results show that high correlation values of 0.852 to 0.959 with the habitual action of the user in different scenarios can be used for auxiliary identity authentication, which is a scalable result for future studies. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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