Estimating Human Movement Using Accelerometers

Tato práce je zaměřena na analýzu lidského pohybu, zejména měření úhlu kolena, co je důležité především při procesu rehabilitace u pacientů s protézy kolenního kloubu nebo po operaci. Pro měření IMU - inerciální měřící jednotky od firmy Xsens jsou použité, přičemž pouze data z 3-osého akcelerometru a gyroskopu jsou zahrnuty. Vhodné umístění jednotek je vybráno, jakož i metody pro kalibraci a následný výpočet úhlu ze zaznamenaných dat. Tyhle metody jsou implementovány a experimentálně ověřeny. Experimenty ukazují, že výsledky jsou docela přesné, a toto řešení je použitelné při analýze pacientů například provedením monitorování chůze.This work is aimed at analysis of human movement, especially measurement of knee angle, which is important data for monitoring in process of rehabilitation on patients with knee arthroplasty or after surgery. For measurement IMUs - Inertial Measurement Units from Xsens are used, while only data from 3-axis accelerometer and gyroscope are gathered. Appropriate unit position is chosen, as well as methods for calibration and angle calculation from stored measured data. These methods are implemented and experimentally validated. Experiments shows, that results are pretty accurate and this solution is useful in analysis of patients for example by doing gait analysis.

Statistical Sensor Fusion of a 9-DoF MEMS IMU for Indoor Navigation

Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8 - 89.5% and the dead-reckoned positioning accuracy by 72.9 - 92.8%, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user's dynamics

Sensing gestures for business intelligence

The combination of sensor data with analytic techniques is growing in popularity for both practitioners and researchers as an Internet of Things (IoT) offers new opportunities and insights. Organisations are trying to use sensor technologies to derive intelligence and gain a competitive edge in their industries. Obtaining data from sensors might not pose too much of a problem, however subsequent utilisation in meeting an organisation’s decision making can be more problematic. Understanding how sensor data analytics can be undertaken is the first step to deriving business intelligence from front line retail environments. This paper explores the use of the Microsoft Kinect sensor to provide intelligence by identifying and sensing gestures to better understand customer behaviour in the retail space

Recognising Complex Activities with Histograms of Relative Tracklets

One approach to the recognition of complex human activities is to use feature descriptors that encode visual inter-actions by describing properties of local visual features with respect to trajectories of tracked objects. We explore an example of such an approach in which dense tracklets are described relative to multiple reference trajectories, providing a rich representation of complex interactions between objects of which only a subset can be tracked. Specifically, we report experiments in which reference trajectories are provided by tracking inertial sensors in a food preparation sce-nario. Additionally, we provide baseline results for HOG, HOF and MBH, and combine these features with others for multi-modal recognition. The proposed histograms of relative tracklets (RETLETS) showed better activity recognition performance than dense tracklets, HOG, HOF, MBH, or their combination. Our comparative evaluation of features from accelerometers and video highlighted a performance gap between visual and accelerometer-based motion features and showed a substantial performance gain when combining features from these sensor modalities. A considerable further performance gain was observed in combination with RETLETS and reference tracklet features

MOCA: A Low-Power, Low-Cost Motion Capture System Based on Integrated Accelerometers

Human-computer interaction (HCI) and virtual reality applications pose the challenge of enabling real-time interfaces for natural interaction. Gesture recognition based on body-mounted accelerometers has been proposed as a viable solution to translate patterns of movements that are associated with user commands, thus substituting point-and-click methods or other cumbersome input devices. On the other hand, cost and power constraints make the implementation of a natural and efficient interface suitable for consumer applications a critical task. Even though several gesture recognition solutions exist, their use in HCI context has been poorly characterized. For this reason, in this paper, we consider a low-cost/low-power wearable motion tracking system based on integrated accelerometers called motion capture with accelerometers (MOCA) that we evaluated for navigation in virtual spaces. Recognition is based on a geometric algorithm that enables efficient and robust detection of rotational movements. Our objective is to demonstrate that such a low-cost and a low-power implementation is suitable for HCI applications. To this purpose, we characterized the system from both a quantitative point of view and a qualitative point of view. First, we performed static and dynamic assessment of movement recognition accuracy. Second, we evaluated the effectiveness of user experience using a 3D game application as a test bed

Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar

Recent studies confirm the applicability of Inertial Measurement Unit (IMU)-based systems for human motion analysis. Notwithstanding, high-end IMU-based commercial solutions are yet too expensive and complex to democratize their use among a wide range of potential users. Less featured entry-level commercial solutions are being introduced in the market, trying to fill this gap, but still present some limitations that need to be overcome. At the same time, there is a growing number of scientific papers using not commercial, but custom do-it-yourself IMU-based systems in medical and sports applications. Even though these solutions can help to popularize the use of this technology, they have more limited features and the description on how to design and build them from scratch is yet too scarce in the literature. The aim of this work is two-fold: (1) Proving the feasibility of building an affordable custom solution aimed at simultaneous multiple body parts orientation tracking; while providing a detailed bottom-up description of the required hardware, tools, and mathematical operations to estimate and represent 3D movement in real-time. (2) Showing how the introduction of a custom 2.4 GHz communication protocol including a channel hopping strategy can address some of the current communication limitations of entry-level commercial solutions. The proposed system can be used for wireless real-time human body parts orientation tracking with up to 10 custom sensors, at least at 50 Hz. In addition, it provides a more reliable motion data acquisition in Bluetooth and Wi-Fi crowded environments, where the use of entry-level commercial solutions might be unfeasible. This system can be used as a groundwork for developing affordable human motion analysis solutions that do not require an accurate kinematic analysis.This research has been partially funded by a research contract with IVECO Spain SL and by the Department of Employment and Industry of Castilla y León (Spain), under research project ErgoTwyn (INVESTUN/21/VA/0003)

Wave Monitoring with Wireless Sensor Networks

Real-time collection of wave information is required for short and long term investigations of natural coastal processes. Current wave monitoring techniques use only point-measurements, which are practical where the bathymetry is relatively uniform. We propose a wave monitoring method that is suitable for places with varying bathymetry, such as coral reefs. Our solution uses a densely deployed wireless sensor network, which allows for a high spatial resolution and 3D monitoring and analysis of the waves. The wireless sensor nodes are equipped with low-cost, low-power, MEMS-based inertial sensing. We report on lab experiments with a Ferris wheel contraption, which is a technique used in practice to evaluate and calibrate the state-of-the-art wave monitoring solutions.\u

Accurate deformation monitoring on bridge structures using a cost-effective sensing system combined with a camera and accelerometers

This is the author accepted manuscript. The final version is available from American Society of Civil Engineers via the DOI in this record.Information on deformation is critical for bridge condition evaluation but accurate characterisation, usually via discrete displacement measurements, remains a challenging task. Vision-based systems are promising tools, possessing advantages of easy installation, low cost and adequate resolution in time and frequency domains. However, vision-based monitoring faces several field challenges and might fail to achieve the required level of working performance in some real-world test conditions e.g.involving low-contrast patterns and mounting instability of optical sensors. To make the best use of the potential of vision-based systems, a mixed sensing system consisting of a consumer-grade camera and an accelerometer is proposed in this study for accurate displacement measurement. The system considers automatic compensation of camera shake and involves autonomous data fusion process for noise reduction. The proposed system is demonstrated through a field monitoring test on a short-span railway bridge and is validated to offer higher accuracy and wider frequency range than using a camera alone. Displacement data by the mixed system are demonstrated to be viable for estimating bridge influence line, indicating the potential for bridge condition assessment