MoCaPose: Motion Capture with Textile-Integrated Capacitive Sensors A New Approach to Wearable Tracking

Skiing is one of the most popular winter sports in the world. Even though the equipment has been improved for preventing injuries during skiing, injury risks of the lower extremity are still high. It is necessary to investigate injury risk by motion analysis during skiing. The wearable motion capture system consisting of inertial sensors and insole pressure sensors can be utilized due to restrictions of conventional optical motion capture system. In this study, the motions for short-and middle-turns during skiing were analyzed using a wearable motion capture system and the multi-scale computer simulation technology. Seven male certified ski coaches participated in this study and their full body motion and foot pressure data were simultaneously recorded by the wearable motion capture system. Joint kinematics and kinetics in the hip, knee and ankle of the right lower extremity were analyzed during short- and middle-turns. Even though a slight difference in the joint kinematics between two turns was predicted, the joint forces and moments for middle-turn were higher than those for short-turn. Because these higher joint forces and moments can result in osteoarthritis or ligament injury at the joint, injury risk of the joint for middle-turn may be higher than that for short-turn. This study confirms that the wearable motion capture system is useful for measuring the motion and plantar pressure data in outdoor sports such as skiing.

A Least Squares Estimate of Satellite Attitude

In order to recognize the human daily activity more easily and accurately, an activity recognition method based on functional features is proposed in this article. Firstly, we transform the data series which collected by the wearable motion capture system into functional data using the techniques of Functional Data Analysis (FDA), thus the underlying continuity and periodicity of motion data can be depicted vividly. The method result indicates that functions of different activities indeed have different patterns. Secondly, a novel kind of feature called functional features including maximum, minimum and frequency of the function are put forward in order to classify different activities. Subsequently, SVM has been employed in order to achieve the highest accuracy according to the functional features. Finally, we compare our algorithm with other state-of-the-art methods so as to prove its effectiveness. Experimental results show that our algorithm can describe the continuity and periodicity of the motion capture data precisely and recognize human periodic activity accurately.

Wearable intelligent device has become a hotspot in the research of human-computer interaction system. A representative example is exoskeleton robot, which has already been applied in rehabilitation, assistance, and even in military field. In order to guarantee man-machine coordination, and to achieve a reasonable performance, proper control has to be implemented according to the wearer’s body motions, and body motion capture system is necessary in this process. In this paper, a wearable body motion capture (WBMC) system is designed based on inertial measurement unit (IMU) and IEEE 802.11b wireless communication, and demonstration application in assistive exoskeleton control validate the proposed WBMC system.

Evaluating the feasibility of two post-hoc correction techniques for mitigating posture-induced measurement errors associated with wearable motion capture

This paper introduces a wearable human motion capture system based on inertial sensor. The sensor nodes are connected by network cables, while the hub node transits data with the computer through Bluetooth. This system not only ensures the data acquisition rate, but also has no movement restrictions for users, such as requiring them to move within a certain range. Using Python programming and Blende engine, the inertial data of each part of the human body is converted into rotation angle and displacement to drive the human model. Meanwhile, rich human-computer interaction functions and visual interface are designed to reduce the difficulty of use. The final motion capture results show that this system has a good performance in recognizing complex human movements, and can be used as a multi-functional motion capture system in motion analysis, film and television production, virtual reality and other fields.

There are several large datasets available that are captured from motion tracking systems which could be useful to train wearable human activity recognition (HAR) systems, if only their spatial data could be mapped into the equivalent inertial measurement unit (IMU) data that would be sensed on the body. In this paper, we describe a mapping from 3D Vicon motion tracking data to data collected from a BlueSense on-body IMU. We characterise the error incurred in order to discern the extent to which it is possible to generate useful training data for a wearable activity recognition system from data collected with a motion capture system. We analyse this by mapping Vicon motion tracking data to rotational velocity and linear acceleration at the head, and compare this to actual gyroscope and accelerometer data collected by an IMU mounted on the head. In a 15 min dataset comprising three static activities—sitting, standing and lying down—we find that 95% of the reconstructed gyroscope data is within an error of [−7.25;+7.46] \(deg \cdot s^{-1}\), while 95% of the reconstructed accelerometer data was contained within [−96.1;+72.9] \(m \cdot G\). However, when we introduce more movement by including data collected while walking this increases to [−19.0;+18.2] \(deg \cdot s^{-1}\) for the gyroscope and [−208;+186] \(m \cdot G\) for the accelerometer. We conclude that generating accurate IMU data from motion capture datasets is possible and could be useful in providing larger volumes of data for activity recognition tasks and in helping enable advanced, data-hungry techniques such as deep learning to be employed on a larger scale within the domain of human activity recognition.

SummaryIndoor and outdoor applications such as sports and health monitoring as well as realistic 3D movie and game animations require ambulatory motion capture. Thus, creating a new low‐cost light‐weight wearable motion capture system that offers realistic motion estimates is of great interest. This paper presents a new approach for ambulatory human motion capture, featuring a body mounted magnetic field source and magnetic field sensors together with an estimation algorithm. A complete study of the model, the hardware, and the estimation algorithms is presented. Results obtained in the context of motion capture of human upper limbs illustrate the proposed approach.Copyright © 2014 John Wiley & Sons, Ltd.

Micro-electromechenical Systems (MEMS) gyroscope is widely used in many occasions to measure the angular speed of the moving objects and attracts the attentions of many research institutions all over the world. This kind of sensor possesses the advantages of high degree of integration, low cost and consumption of power. This paper first introduces the research development of silicon MEMS gyroscope since eighties of last century; the researches of many institutions such as Draper Laboratory and UC Berkeley are mentioned and different design principles, control methods and structures are presented. This review then presents the key theories and technologies of the sensor and some research results of them. In additional, some recent new applications of MEMS gyroscope are also been introduced in this paper such as wearable motion capture system and micro inertial measurement unit. Finally, according to the review, some views of silicon MEMS gyroscope and its future prospects are put forwarded.

Towards remote monitoring of Parkinson’s disease tremor using wearable motion capture systems

Motion capture systems are gaining much attention in various fields, including entertainment, medical and sports fields. Although many types of motion capture sensor have been emerging, they have limitations and disadvantages such as occlusion, drift and interference by electromagnetic fields. Here, we introduce the novel wearable motion capture system using fiber Bragg gratings (FBGs) sensors. Since the human joints have different degrees of freedom (DOF), we developed three types of sensors to reconstruct the human body motion from the strains induced on the FBGs. First, a shape sensor using three fibers provides the position and orientation of joints in three dimensional space. Second, we introduce the angle sensor which is capable of measuring bending angle with high curvature using single fiber. Lastly, to detect the twisting of joints, a sensor with fiber attached on a soft material spirally is used. With the optical fiber based motion capture sensors, we reconstruct the motion of arm in realtime. In detail, the joints of the arm include the sternoclavicular, acromioclavicular, shoulder and elbow. By arranging the three types of sensors on the joints in accordance with the DOF, the accuracy of the reconstructed motion is evaluated, resulting in an average error below 2.42°. Finally, to prove the feasibility of applying in virtual reality, we successfully manipulate the virtual avatar in real-time.

Inertial measurement units (IMUs) are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. Wearable motion capture systems based on IMUs have been advertised as alternatives to optical motion capture. In this paper, the accuracy of five different IMUs of the same type in measuring 3D orientation in static situations, as well as the calibration of the accelerometers and magnetometers within the IMUs, has been investigated. The maximum absolute static orientation error was 5.2°, higher than the 1° claimed by the vendor. If the IMUs are re-calibrated at the time of measurement with the re-calibration procedure described in this paper, it is possible to obtain an error of less than 1°, in agreement with the vendor's specifications (XSens Technologies B.V. 2005. Motion tracker technical documentation Mtx-B. Version 1.03. Available from: www.xsens.com). The new calibration appears to be valid for at least 22 days providing the sensor is not exposed to high impacts. However, if several sensors are ‘daisy chained’ together changes to the magnetometer bias can cause heading errors of up to 15°. The results demonstrate the non-linear relationship between the vendor's orthogonality claim of < 0.1° and the accuracy of 3D orientation obtained from factory calibrated IMUs in static situations. The authors hypothesise that the high magnetic dip (64°) in our laboratory may have exacerbated the errors reported. For biomechanical research, small relative movements of a body segment from a calibrated position are likely to be more accurate than large scale global motion that may have an error of up to 9.8°.

Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%.

Motion capture system can transform motion information of human body into digital data for analysis and processing in computer or cyberspace. It is an important medium to connect physical space and cyberspace. Aiming at the problems of high cost and high power consumption of existing inertial motion capture systems, this paper proposes a wearable wireless motion capture system based on nine-axis inertial sensor and Bluetooth Low Energy technology. The system can capture the motion trajectory of human body and realize real-time human motion display and data storage in computer. An explicit complementary filtering algorithm based on proportional integral model is used in the process of character motion update. Motion capture experiments are carried out on a human body and results show that the system has sound dynamic performance with the error less than 2 degrees and can achieve efficient human motion capture. The sampling frequency of the system reaches 125Hz and the working time is up to 10 hours. The system can serve as the basis for developing affordable human motion capture solutions that require long-term testing without precise kinematic analysis.

Research in Human-Computer Interaction (HCI) has gained interest in recent years and has fostered new ideas and expectations. There are many kinds of human motion capture methods nowadays. In this paper, we have compared 2 kinds of methods which are the most popular methods in HCI - Microsoft Kinect Sensor and Inertial Sensors. This paper presents a wearable real-time human motion capture system using inertial sensors, and result of our method has been compared with Microsoft Kinect Sensor. Several experiments have been performed to validate the effectiveness of our method.