Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients

Validation of joint angle measurements: comparison of a novel low cost marker-less system with an industry standard marker-based system

Human motion tracking is widely used for assessment of movement dysfunction in orthopaedic patients. Currently, most clinical motion analysis centres use marker based three-dimensional (3D) systems as they are deemed to be the most accurate method. However, due to space, costs and logistics they are not available in many clinical settings. This study compared joint angles measured in functional tests using the novel low-cost Microsoft Kinect Perfect Phorm system with the established marker based Nexus VICON system. When measuring right and left knee flexion, the average difference between the VICON and Kinect Perfect Phorm measurement was 13.2%, with a SD of 19.6. Both overestimation and underestimation of the joint angle was recorded in different participants. Although the average percentage difference during hip abduction tests was lower at -3.9%, the range of error was far greater (SD=75). From this, it can be concluded that the level of accuracy presented in the new low cost Kinect Perfect Phorm system is not yet suitable for clinical assessments. However, for general tests of performance, and for tracking cases where absolute accuracy is less critical, future versions of this software may have a place

Human Gait Model Development for Objective Analysis of Pre/Post Gait Characteristics Following Lumbar Spine Surgery

Although multiple advanced tools and methods are available for gait analysis, the gait and its related disorders are usually assessed by visual inspection in the clinical environment. This thesis aims to introduce a gait analysis system that provides an objective method for gait evaluation in clinics and overcomes the limitations of the current gait analysis systems. Early identification of foot drop, a common gait disorder, would become possible using the proposed methodology

Markerless assisted rehabilitation system

The project focuses on the use of modern technology to analyze human movement. This analysis turns out to be useful aid for physicians in rehabilitation of patients with limb injuries. This method is more precise than simple observation of the patient through the organ of sight. The proposed system allows markerless determination of deviations between the selected bones and joints, and as a result do not require specialized and expensive equipment. The implemented application presents instructional animation of the exercises and verify the correctness of its performance in real time. The equipment that meets the requirements of the project is the Microsoft Kinect, which is nowadays widely used in the medical field

An approach to physical rehabilitation using state-of-the-art virtual reality and motion tracking technologies

Proceedings of Conference on ENTERprise Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist 2015 October 7-9, 2015This work was partially funded by European Union’s CIP Programme (ICT-PSP-2012) under grant agreement no. 325146 (SEACW project)

Wearable Internet of Things - from Human Activity Tracking to Clinical Integration

Wearable devices for human activity tracking have been rapidly emerging. Most of them are capable of sending health statistics to smartphones, smartwatches or smart bands. However, they only provide the data for individual analysis and their data is not integrated into clinical practice. Leveraging on the Internet of Things (IoT), edge and cloud computing technologies, we propose an architecture which is capable of providing cloud based clinical services using human activity data. Such services could supplement the shortage of staff in primary healthcare centers thereby reducing the burden on healthcare service providers. The enormous amount of data created from such services could also be utilized for planning future therapies by studying recovery cycles of existing patients. We provide a prototype based on our architecture and discuss its salient features. We also provide use cases of our system in personalized and home based healthcare services. We propose an International Telecommunication Union based standardization (ITU-T) for our design and discuss future directions in wearable IoT

Validation of foot pitch angle estimation using inertial measurement unit against marker-based optical 3D motion capture system

Gait analysis is relevant to a broad range of clinical applications in areas of orthopedics, neurosurgery, rehabilitation and the sports medicine. There are various methods available for capturing and analyzing the gait cycle. Most of gait analysis methods are computationally expensive and difficult to implement outside the laboratory environment. Inertial measurement units, IMUs are considered a promising alternative for the future of gait analysis. This study reports the results of a systematic validation procedure to validate the foot pitch angle measurement captured by an IMU against Vicon Optical Motion Capture System, considered the standard method of gait analysis. It represents the first phase of a research project which aims to objectively evaluate the ankle function and gait patterns of patients with dorsiflexion weakness (commonly called a “drop foot”) due to a L5 lumbar radiculopathy pre- and post-lumbar decompression surgery. The foot pitch angle of 381 gait cycles from 19 subjects walking trails on a flat surface have been recorded throughout the course of this study. Comparison of results indicates a mean correlation of 99.542% with a standard deviation of 0.834%. The maximum root mean square error of the foot pitch angle measured by the IMU compared with the Vicon Optical Motion Capture System was 3.738° and the maximum error in the same walking trail between two measurements was 9.927°. These results indicate the level of correlation between the two systems

Clustering and Analysis of User Motions to Enhance Human Learning: A First Study Case with the Flip Bottle Challenge

International audienceMore and more domains such as industry, sport, medicine, Human Computer Interaction (HCI) and education analyze user motions to observe human behavior, follow and predict its action, intention and emotion, to interact with computer systems and enhance user experience in Virtual (VR) and Augmented Reality (AR). In the context of human learning of movements, existing software applications and methods rarely use 3D captured motions for pedagogical feedback. This comes from several issues related to the highly complex and dimensional nature of these data, and by the need to correlate this information with the observation needs of the teacher. Such issues could be solved by the use of machine learning techniques, which could provide efficient and complementary feedback in addition to the expert advice, from motion data. The context of the presented work is the improvement of the human learning process of a motion, based on clustering techniques. The main goal is to give advice according to the analysis of clusters representing user profiles during a learning situation. To achieve this purpose, a first step is to work on the separation of the motions into different categories according to a set of well-chosen features. In this way, allowing a better and more accurate analysis of the motion characteristics is expected. An experimentation was conducted with the Bottle Flip Challenge. Human motions were first captured and filtered, in order to compensate for hardware related errors. Descriptors related to speed and acceleration are then computed, and used in two different automatic approaches. The first one tries to separate the motions, using the computed descriptors, and the second one, compares the obtained separation with the ground truth. The results show that, while the obtained partitioning is not relevant to the degree of success of the task, the data are separable using the descriptors