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Sensor Systems for Motion Capture and Interpretation

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

Deadline for manuscript submissions: closed (15 July 2015) | Viewed by 115200

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Special Issue Editor

Prof. Dr. Oliver Amft

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Chair of Sensor Technology, ACTLab research group, University of Passau, Innstrasse 43, D-94032 Passau, Germany
Interests: wearable computing, pattern recognition, ubiquitous computing, biomedical engineering, gesture recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue addresses sensors for motion capture connected with sensor data interpretation using signal analysis and pattern recognition methods. Motion sensing and interpretation has gained wide interest for various areas, including context awareness, augmented reality, human computer interaction, and applications, e.g., in sports, rehabilitation, gaming, and many others. Depending on the application, motion sensing is often done using specific sensor designs and sensor modalities optimized for use at the human body, on objects, or embedded in the environment.

This special issue invites novel contributions that couple motion sensor design, integration with signal processing and machine learning methods to acquire and interpret sensor data continuously or from spot measurements. Contributions will utilize real-world evaluations, or highly insightful and realistic simulation approaches. Topics may include, but are not limited to:

Novel motion sensor design coupled with appropriate signal processing
Unobtrusive motion sensing and motion pattern interpretation.
Movement performance analysis in real-life settings.
Sensor systems for gesture recognition, virtual motion representation, and augmented reality and their application.

Prof. Dr. Oliver Amft
Guest Editor

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Keywords

movement analysis
biomechanics
biosensors
rehabilitation
gaming
interaction
inertial sensors

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Published Papers (10 papers)

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Research

2229 KiB

Open AccessArticle

Wearable Goniometer and Accelerometer Sensory Fusion for Knee Joint Angle Measurement in Daily Life

by Alessandro Tognetti, Federico Lorussi, Nicola Carbonaro and Danilo De Rossi

Sensors 2015, 15(11), 28435-28455; https://doi.org/10.3390/s151128435 - 11 Nov 2015

Cited by 81 | Viewed by 17821

Abstract

Human motion analysis is crucial for a wide range of applications and disciplines. The development and validation of low cost and unobtrusive sensing systems for ambulatory motion detection is still an open issue. Inertial measurement systems and e-textile sensors are emerging as potential technologies for daily life situations. We developed and conducted a preliminary evaluation of an innovative sensing concept that combines e-textiles and tri-axial accelerometers for ambulatory human motion analysis. Our sensory fusion method is based on a Kalman filter technique and combines the outputs of textile electrogoniometers and accelerometers without making any assumptions regarding the initial accelerometer position and orientation. We used our technique to measure the flexion-extension angle of the knee in different motion tasks (monopodalic flexions and walking at different velocities). The estimation technique was benchmarked against a commercial measurement system based on inertial measurement units and performed reliably for all of the various tasks (mean and standard deviation of the root mean square error of 1:96 and 0:96, respectively). In addition, the method showed a notable improvement in angular estimation compared to the estimation derived by the textile goniometer and accelerometer considered separately. In future work, we will extend this method to more complex and multi-degree of freedom joints. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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5713 KiB

Open AccessArticle

Multi-Sensor Calibration of Low-Cost Magnetic, Angular Rate and Gravity Systems

by Markus Lüken, Berno J.E. Misgeld, Daniel Rüschen and Steffen Leonhardt

Sensors 2015, 15(10), 25919-25936; https://doi.org/10.3390/s151025919 - 13 Oct 2015

Cited by 17 | Viewed by 8347

Abstract

We present a new calibration procedure for low-cost nine degrees-of-freedom (9DOF) magnetic, angular rate and gravity (MARG) sensor systems, which relies on a calibration cube, a reference table and a body sensor network (BSN). The 9DOF MARG sensor is part of our recently-developed “Integrated Posture and Activity Network by Medit Aachen” (IPANEMA) BSN. The advantage of this new approach is the use of the calibration cube, which allows for easy integration of two sensor nodes of the IPANEMA BSN. One 9DOF MARG sensor node is thereby used for calibration; the second 9DOF MARG sensor node is used for reference measurements. A novel algorithm uses these measurements to further improve the performance of the calibration procedure by processing arbitrarily-executed motions. In addition, the calibration routine can be used in an alignment procedure to minimize errors in the orientation between the 9DOF MARG sensor system and a motion capture inertial reference system. A two-stage experimental study is conducted to underline the performance of our calibration procedure. In both stages of the proposed calibration procedure, the BSN data, as well as reference tracking data are recorded. In the first stage, the mean values of all sensor outputs are determined as the absolute measurement offset to minimize integration errors in the derived movement model of the corresponding body segment. The second stage deals with the dynamic characteristics of the measurement system where the dynamic deviation of the sensor output compared to a reference system is Sensors 2015, 15 25920 corrected. In practical validation experiments, this procedure showed promising results with a maximum RMS error of 3.89°. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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720 KiB

Open AccessArticle

Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy

by Juri Taborri, Emilia Scalona, Eduardo Palermo, Stefano Rossi and Paolo Cappa

Sensors 2015, 15(9), 24514-24529; https://doi.org/10.3390/s150924514 - 23 Sep 2015

Cited by 61 | Viewed by 7449

Abstract

Gait-phase recognition is a necessary functionality to drive robotic rehabilitation devices for lower limbs. Hidden Markov Models (HMMs) represent a viable solution, but they need subject-specific training, making data processing very time-consuming. Here, we validated an inter-subject procedure to avoid the intra-subject one in two, four and six gait-phase models in pediatric subjects. The inter-subject procedure consists in the identification of a standardized parameter set to adapt the model to measurements. We tested the inter-subject procedure both on scalar and distributed classifiers. Ten healthy children and ten hemiplegic children, each equipped with two Inertial Measurement Units placed on shank and foot, were recruited. The sagittal component of angular velocity was recorded by gyroscopes while subjects performed four walking trials on a treadmill. The goodness of classifiers was evaluated with the Receiver Operating Characteristic. The results provided a goodness from good to optimum for all examined classifiers (0 < G < 0.6), with the best performance for the distributed classifier in two-phase recognition (G = 0.02). Differences were found among gait partitioning models, while no differences were found between training procedures with the exception of the shank classifier. Our results raise the possibility of avoiding subject-specific training in HMM for gait-phase recognition and its implementation to control exoskeletons for the pediatric population. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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2771 KiB

Open AccessArticle

A Non-Contact Measurement System for the Range of Motion of the Hand

by Trieu Pham, Pubudu N. Pathirana, Hieu Trinh and Pearse Fay

Sensors 2015, 15(8), 18315-18333; https://doi.org/10.3390/s150818315 - 28 Jul 2015

Cited by 25 | Viewed by 17081

Abstract

An accurate and standardised tool to measure the active range of motion (ROM) of the hand is essential to any progressive assessment scenario in hand therapy practice. Goniometers are widely used in clinical settings for measuring the ROM of the hand. However, such measurements have limitations with regard to inter-rater and intra-rater reliability and involve direct physical contact with the hand, possibly increasing the risk of transmitting infections. The system proposed in this paper is the first non-contact measurement system utilising Intel Perceptual Technology and a Senz3D Camera for measuring phalangeal joint angles. To enhance the accuracy of the system, we developed a new approach to achieve the total active movement without measuring three joint angles individually. An equation between the actual spacial position and measurement value of the proximal inter-phalangeal joint was established through the measurement values of the total active movement, so that its actual position can be inferred. Verified by computer simulations, experimental results demonstrated a significant improvement in the calculation of the total active movement and successfully recovered the actual position of the proximal inter-phalangeal joint angles. A trial that was conducted to examine the clinical applicability of the system involving 40 healthy subjects confirmed the practicability and consistency in the proposed system. The time efficiency conveyed a stronger argument for this system to replace the current practice of using goniometers. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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1130 KiB

Open AccessArticle

Estimation of Joint Forces and Moments for the In-Run and Take-Off in Ski Jumping Based on Measurements with Wearable Inertial Sensors

by Grega Logar and Marko Munih

Sensors 2015, 15(5), 11258-11276; https://doi.org/10.3390/s150511258 - 13 May 2015

Cited by 34 | Viewed by 9282

Abstract

This study uses inertial sensors to measure ski jumper kinematics and joint dynamics, which was until now only a part of simulation studies. For subsequent calculation of dynamics in the joints, a link-segment model was developed. The model relies on the recursive Newton–Euler inverse dynamics. This approach allowed the calculation of the ground reaction force at take-off. For the model validation, four ski jumpers from the National Nordic center performed a simulated jump in a laboratory environment on a force platform; in total, 20 jumps were recorded. The results fit well to the reference system, presenting small errors in the mean and standard deviation and small root-mean-square errors. The error is under 12% of the reference value. For field tests, six jumpers participated in the study; in total, 28 jumps were recorded. All of the measured forces and moments were within the range of prior simulated studies. The proposed system was able to indirectly provide the values of forces and moments in the joints of the ski-jumpers’ body segments, as well as the ground reaction force during the in-run and take-off phases in comparison to the force platform installed on the table. Kinematics assessment and estimation of dynamics parameters can be applied to jumps from any ski jumping hill. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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7321 KiB

Open AccessArticle

Gait Measurement System for the Multi-Target Stepping Task Using a Laser Range Sensor

by Ayanori Yorozu, Shu Nishiguchi, Minoru Yamada, Tomoki Aoyama, Toshiki Moriguchi and Masaki Takahashi

Sensors 2015, 15(5), 11151-11168; https://doi.org/10.3390/s150511151 - 13 May 2015

Cited by 13 | Viewed by 7949

Abstract

For the prevention of falling in the elderly, gait training has been proposed using tasks such as the multi-target stepping task (MTST), in which participants step on assigned colored targets. This study presents a gait measurement system using a laser range sensor for the MTST to evaluate the risk of falling. The system tracks both legs and measures general walking parameters such as stride length and walking speed. Additionally, it judges whether the participant steps on the assigned colored targets and detects cross steps to evaluate cognitive function. However, situations in which one leg is hidden from the sensor or the legs are close occur and are likely to lead to losing track of the legs or false tracking. To solve these problems, we propose a novel leg detection method with five observed leg patterns and global nearest neighbor-based data association with a variable validation region based on the state of each leg. In addition, methods to judge target steps and detect cross steps based on leg trajectory are proposed. From the experimental results with the elderly, it is confirmed that the proposed system can improve leg-tracking performance, judge target steps and detect cross steps with high accuracy. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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1134 KiB

Open AccessArticle

Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement

by Shuxiang Guo, Muye Pang, Baofeng Gao, Hideyuki Hirata and Hidenori Ishihara

Sensors 2015, 15(4), 9022-9038; https://doi.org/10.3390/s150409022 - 16 Apr 2015

Cited by 70 | Viewed by 9055

Abstract

The surface electromyography (sEMG) technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The purpose of this paper is to compare eight combinations of four feature extraction methods (Root Mean Square (RMS), Detrended Fluctuation Analysis (DFA), Weight Peaks (WP), and Muscular Model (MM)) and two classifiers (Neural Networks (NN) and Support Vector Machine (SVM)), for the task of mapping sEMG signals to eight upper-limb motions, to find out the relation between these methods and propose a proper combination to solve this issue. Seven subjects participated in the experiment and six muscles of the upper-limb were selected to record sEMG signals. The experimental results showed that NN classifier obtained the highest recognition accuracy rate (88.7%) during the training process while SVM performed better in real-time experiments (85.9%). For time consumption, SVM took less time than NN during the training process but needed more time for real-time computation. Among the four feature extraction methods, WP had the highest recognition rate for the training process (97.7%) while MM performed the best during real-time tests (94.3%). The combination of MM and NN is recommended for strict real-time applications while a combination of MM and SVM will be more suitable when time consumption is not a key requirement. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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1620 KiB

Open AccessArticle

Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data

by Jens Barth, Cäcilia Oberndorfer, Cristian Pasluosta, Samuel Schülein, Heiko Gassner, Samuel Reinfelder, Patrick Kugler, Dominik Schuldhaus, Jürgen Winkler, Jochen Klucken and Björn M. Eskofier

Sensors 2015, 15(3), 6419-6440; https://doi.org/10.3390/s150306419 - 17 Mar 2015

Cited by 153 | Viewed by 13637

Abstract

Changes in gait patterns provide important information about individuals’ health. To perform sensor based gait analysis, it is crucial to develop methodologies to automatically segment single strides from continuous movement sequences. In this study we developed an algorithm based on time-invariant template matching to isolate strides from inertial sensor signals. Shoe-mounted gyroscopes and accelerometers were used to record gait data from 40 elderly controls, 15 patients with Parkinson’s disease and 15 geriatric patients. Each stride was manually labeled from a straight 40 m walk test and from a video monitored free walk sequence. A multi-dimensional subsequence Dynamic Time Warping (msDTW) approach was used to search for patterns matching a pre-defined stride template constructed from 25 elderly controls. F-measure of 98% (recall 98%, precision 98%) for 40 m walk tests and of 97% (recall 97%, precision 97%) for free walk tests were obtained for the three groups. Compared to conventional peak detection methods up to 15% F-measure improvement was shown. The msDTW proved to be robust for segmenting strides from both standardized gait tests and free walks. This approach may serve as a platform for individualized stride segmentation during activities of daily living. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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1715 KiB

Open AccessArticle

Depth Camera-Based 3D Hand Gesture Controls with Immersive Tactile Feedback for Natural Mid-Air Gesture Interactions

by Kwangtaek Kim, Joongrock Kim, Jaesung Choi, Junghyun Kim and Sangyoun Lee

Sensors 2015, 15(1), 1022-1046; https://doi.org/10.3390/s150101022 - 8 Jan 2015

Cited by 45 | Viewed by 10181

Abstract

Vision-based hand gesture interactions are natural and intuitive when interacting with computers, since we naturally exploit gestures to communicate with other people. However, it is agreed that users suffer from discomfort and fatigue when using gesture-controlled interfaces, due to the lack of physical feedback. To solve the problem, we propose a novel complete solution of a hand gesture control system employing immersive tactile feedback to the user’s hand. For this goal, we first developed a fast and accurate hand-tracking algorithm with a Kinect sensor using the proposed MLBP (modified local binary pattern) that can efficiently analyze 3D shapes in depth images. The superiority of our tracking method was verified in terms of tracking accuracy and speed by comparing with existing methods, Natural Interaction Technology for End-user (NITE), 3D Hand Tracker and CamShift. As the second step, a new tactile feedback technology with a piezoelectric actuator has been developed and integrated into the developed hand tracking algorithm, including the DTW (dynamic time warping) gesture recognition algorithm for a complete solution of an immersive gesture control system. The quantitative and qualitative evaluations of the integrated system were conducted with human subjects, and the results demonstrate that our gesture control with tactile feedback is a promising technology compared to a vision-based gesture control system that has typically no feedback for the user’s gesture inputs. Our study provides researchers and designers with informative guidelines to develop more natural gesture control systems or immersive user interfaces with haptic feedback. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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20992 KiB

Open AccessArticle

Evaluation of the Leap Motion Controller as a New Contact-Free Pointing Device

by Daniel Bachmann, Frank Weichert and Gerhard Rinkenauer

Sensors 2015, 15(1), 214-233; https://doi.org/10.3390/s150100214 - 24 Dec 2014

Cited by 111 | Viewed by 13026

Abstract

This paper presents a Fitts’ law-based analysis of the user’s performance in selection tasks with the Leap Motion Controller compared with a standard mouse device. The Leap Motion Controller (LMC) is a new contact-free input system for gesture-based human-computer interaction with declared sub-millimeter accuracy. Up to this point, there has hardly been any systematic evaluation of this new system available. With an error rate of 7.8% for the LMC and 2.8% for the mouse device, movement times twice as large as for a mouse device and high overall effort ratings, the Leap Motion Controller’s performance as an input device for everyday generic computer pointing tasks is rather limited, at least with regard to the selection recognition provided by the LMC. Full article

(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)

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