An inertial motion capture framework for constructing body sensor networks

Motion capture is the process of measuring and subsequently reconstructing the movement of an animated object or being in virtual space. Virtual reconstructions of human motion play an important role in numerous application areas such as animation, medical science, ergonomics, etc. While optical motion capture systems are the industry standard, inertial body sensor networks are becoming viable alternatives due to portability, practicality and cost. This thesis presents an innovative inertial motion capture framework for constructing body sensor networks through software environments, smartphones and web technologies. The first component of the framework is a unique inertial motion capture software environment aimed at providing an improved experimentation environment, accompanied by programming scaffolding and a driver development kit, for users interested in studying or engineering body sensor networks. The software environment provides a bespoke 3D engine for kinematic motion visualisations and a set of tools for hardware integration. The software environment is used to develop the hardware behind a prototype motion capture suit focused on low-power consumption and hardware-centricity. Additional inertial measurement units, which are available commercially, are also integrated to demonstrate the functionality the software environment while providing the framework with additional sources for motion data. The smartphone is the most ubiquitous computing technology and its worldwide uptake has prompted many advances in wearable inertial sensing technologies. Smartphones contain gyroscopes, accelerometers and magnetometers, a combination of sensors that is commonly found in inertial measurement units. This thesis presents a mobile application that investigates whether the smartphone is capable of inertial motion capture by constructing a novel omnidirectional body sensor network. This thesis proposes a novel use for web technologies through the development of the Motion Cloud, a repository and gateway for inertial data. Web technologies have the potential to replace motion capture file formats with online repositories and to set a new standard for how motion data is stored. From a single inertial measurement unit to a more complex body sensor network, the proposed architecture is extendable and facilitates the integration of any inertial hardware configuration. The Motion Cloud’s data can be accessed through an application-programming interface or through a web portal that provides users with the functionality for visualising and exporting the motion data

A visualization framework for the analysis ofneuromuscularsimulations

We present a visualization framework for exploring and analyzing data sets from biomechanical and neuromuscular simulations. These data sets describe versatile information related to the different stages of a motion analysis. In studying these data using a 3D visualization approach, interactive exploring is important, especially for supporting spatial analysis. Moreover, as these data contain many various but related elements, numerical analysis of neuromuscular simulations is complicated. Visualization techniques enhance the analysis process, thus improving the effectiveness of the experiments. Our approach allows convenient definitions of relationships between numerical data sets and 3D objects. Scientific simulation data sets appropriate for this style of analysis are present everywhere motion analysis is performed and are predominant in many clinical works. In this paper, we outline the functionalities of the framework as well as applications embedded within the OpenSim simulation platform. These functionalities form an effective approach specifically designed for the investigation of neuromuscular simulations. This claim is supported by evaluation experiments where the framework was used to analyze gaits and crouch motion

Human motion analysis and simulation tools: a survey

Computational systems to identify objects represented in image sequences and tracking their motion in a fully automatic manner, enabling a detailed analysis of the involved motion and its simulation are extremely relevant in several fields of our society. In particular, the analysis and simulation of the human motion has a wide spectrum of relevant applications with a manifest social and economic impact. In fact, usage of human motion data is fundamental in a broad number of domains (e.g.: sports, rehabilitation, robotics, surveillance, gesture-based user interfaces, etc.). Consequently, many relevant engineering software applications have been developed with the purpose of analyzing and/or simulating the human motion. This chapter presents a detailed, broad and up to date survey on motion simulation and/or analysis software packages that have been developed either by the scientific community or commercial entities. Moreover, a main contribution of this chapter is an effective framework to classify and compare motion simulation and analysis tools

Application and Evaluation of Lighthouse Technology for Precision Motion Capture

This thesis presents the development towards a system that can capture and quantify motion for applications in biomechanical and medical fields demanding precision motion tracking using the lighthouse technology. Commercially known as SteamVR tracking, the lighthouse technology is a motion tracking system developed for virtual reality applications that makes use of patterned infrared light sources to highlight trackers (objects embedded with photodiodes) to obtain their pose or spatial position and orientation. Current motion capture systems such as the camera-based motion capture are expensive and not readily available outside of research labs. This thesis provides a case for low-cost motion capture systems. The technology is applied to quantify motion to draw inferences about biomechanics capture and analysis, quantification of gait, and prosthetic alignment. Possible shortcomings for data acquisition using this system for the stated applications have been addressed. The repeatability of the system has been established by determining the standard deviation error for multiple trials based on a motion trajectory using a seven degree-of-freedom robot arm. The accuracy testing for the system is based on cross-validation between the lighthouse technology data and transformations derived using joint angles by developing a forward kinematics model for the robot’s end-effector pose. The underlying principle for motion capture using this system is that multiple trackers placed on limb segments allow to record the position and orientation of the segments in relation to a set global frame. Joint angles between the segments can then be calculated from the recorded positions and orientations of each tracker using inverse kinematics. In this work, inverse kinematics for rigid bodies was based on calculating homogeneous transforms to the individual trackers in the model’s reference frame to find the respective Euler angles as well as using the analytical approach to solve for joint variables in terms of known geometric parameters. This work was carried out on a phantom prosthetic limb. A custom application-specific motion tracker was also developed using a hardware development kit which would be further optimized for subsequent studies involving biomechanics motion capture

A mobile cloud computing framework integrating multilevel encoding for performance monitoring in telerehabilitation

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment