Human gait assessment using a 3D marker-less multimodal motion capture system

Gait analysis is the measurement, processing and systematic interpretation of biomechanical parameters that characterize human locomotion. It supports the identification of movement limitations and development of rehabilitation procedures. Accurate Gait analysis is important in sports analysis, medical field, and rehabilitation. Although Gait analysis is performed in several laboratories in many countries, there are many issues such as: (i) the high cost of precise Motion Capture systems; (ii) the scarcity of qualified personnel to operate them; (iii) expertise required to interpret their results; (iv) space requirements to install and store these systems; as well as difficulties related to the measurement protocols of each system; (vi) limited availability (vii) and the use of markers can be a barrier for some clinical use cases (e.g. patients recovering from orthopedics surgeries). In this work, we present a low cost and more accessible system based on the integration of a Multiple Microsoft Kinect sensors and multiple Shimmer inertial sensors to capture human Gait. The novel multimodal system combines data from inertial and 3D depth cameras and outputs spatiotemporal Gait variables. A comparison of this system with the VICON system (the gold standard in Motion Capture) was performed. Our relatively low-cost marker-less multimodal motion generates a complete 360-degree skeleton view. We compare our system with the VICON via gait spatiotemporal variables: Gait cycle time, stride time, Gait length (distance between two strides), stride length, and velocity. The system was also evaluated with knee and hip joint angles measurement accuracy. The results show high correlation for spatiotemporal variables and joint angles inside the 95% bootstrap prediction when compared with VICON

A multi-camera and multimodal dataset for posture and gait analysis

Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user’s progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device.This work has been supported by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant 2020.05708.BD and under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Cognitive-motor interference in people with multiple sclerosis: a kinematic approach to clarify the effect of cognitive load on walking performance

The simultaneous performance of cognitive tasks during locomotion (or cognitive-motor dual-task) is known to cause performance deficits in either one of, or both tasks. Furthermore, these performance decrements are exacerbated by the presence of motor impairments and cognitive dysfunctions characteristic of numerous neurological diseases, such as multiple sclerosis (MS). In this regard the assessment of walking while performing a cognitive task may represent a relevant outcome measure, because it allows measuring, in a laboratory setting, individual’s ability to cope with walking challenging situations similar to everyday living. The first aim of this thesis is to provide an experimental setup, based on the use of optoelectronic stereophotogrammetry, for obtaining quantitative evaluation of walking biomechanics and motor strategies during dual-task performance in both healthy adults and people with MS (pwMS). Then, this experimental methodology is tested as suitable method not only for detecting, measuring and characterizing disability, but also for testing intervention effectiveness in clinical practice. Specifically, the study is focused on the assessment of spatiotemporal parameters and lower limb kinematics during single- (normal pace walking) and dual-task (walking while performing a discrimination and decision-making). This thesis is composed of four experiments. The first two aimed to measure the effect of cognitive-motor interference on walking biomechanics in terms of spatiotemporal parameters and lower limb joint kinematics. In this regard, a sample of pwMS stratified by disability level (low disability, EDSS 1.0-2.5, n=37; mild to moderate disability, EDSS 3.0-6.0, n=44) and a sample of age- and gender-matched healthy adults (n=41) underwent a 3D kinematic evaluation of single- and dual-task performance using a motion capture system. Differences between conditions and groups were investigated using a two-way repeated ANOVA. The results reported that gait speed and stride length were sensitive motor variables in detecting differences from single- to dual-task condition in both pwMS and unaffected individuals, whereas spatiotemporal parameters closely related to balance control (e.g. step width, double support phase duration) were sensitive to changes only in pwMS with moderate disability. Moreover, those patients showed significant changes in the kinematics of distal joint (shank-foot) and proximal joint (hip), including a reduction in ankle plantarflexion and hip extension peak at the terminal stance phase. These observed changes in more impaired patients are compensatory mechanism to stabilize body posture and allow safe locomotion during complicate dual-task activities. Finally, the other two experiments were designed to provide a clinical application of this methodology, as a tool for quantitatively assessing biomechanics changes after an innovative therapeutic intervention. In this regard, a sample of pwMS (n=34) with mild to moderate disability participated in a bicentric clinical trial. As per protocol, pwMS completed an intervention consisting of either active or sham multiple sessions of transcranial direct current stimulation (tDCS) combined with physical activity, aimed to improve walking performance. Following repeated application of active tDCS, the results obtained from the quantitative gait analysis showed greater improvements in gait velocity, step length and walking endurance. This improvement measured in walking had corresponding effects on walking dual-task performance. In fact, the dual-task cost of gait parameters was significantly reduced after the active tDCS intervention. In conclusion, the quantitative assessment of walking impairments during the execution of functional task in pwMS can support a deep learning of both movement features and motor strategies, which should have implications for the design and validation of clinical intervention aimed at improving functional walking

Markerless Human Motion Analysis

Measuring and understanding human motion is crucial in several domains, ranging from neuroscience, to rehabilitation and sports biomechanics. Quantitative information about human motion is fundamental to study how our Central Nervous System controls and organizes movements to functionally evaluate motor performance and deficits. In the last decades, the research in this field has made considerable progress. State-of-the-art technologies that provide useful and accurate quantitative measures rely on marker-based systems. Unfortunately, markers are intrusive and their number and location must be determined a priori. Also, marker-based systems require expensive laboratory settings with several infrared cameras. This could modify the naturalness of a subject\u2019s movements and induce discomfort. Last, but not less important, they are computationally expensive in time and space. Recent advances on markerless pose estimation based on computer vision and deep neural networks are opening the possibility of adopting efficient video-based methods for extracting movement information from RGB video data. In this contest, this thesis presents original contributions to the following objectives: (i) the implementation of a video-based markerless pipeline to quantitatively characterize human motion; (ii) the assessment of its accuracy if compared with a gold standard marker-based system; (iii) the application of the pipeline to different domains in order to verify its versatility, with a special focus on the characterization of the motion of preterm infants and on gait analysis. With the proposed approach we highlight that, starting only from RGB videos and leveraging computer vision and machine learning techniques, it is possible to extract reliable information characterizing human motion comparable to that obtained with gold standard marker-based systems

Gait Recognition

Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, human‐computer interaction and so on. In this chapter, we introduce the state‐of‐the‐art gait recognition techniques, which include 3D‐based and 2D‐based methods, in the first part. And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be view‐invariant, in the third part. And some gait‐based applications are introduced

Exploring the Development Requirements for Virtual Reality Gait Analysis

The hip joint is highly prone to traumatic and degenerative pathologies resulting in irregular locomotion. Monitoring and treatment depend on high-end technology facilities requiring physician and patient co-location, thus limiting access to specialist monitoring and treatment for populations living in rural and remote locations. Telemedicine offers an alternative means of monitoring, negating the need for patient physical presence. In addition, emerging technologies, such as virtual reality (VR) and immersive technologies, offer potential future solutions through virtual presence, where the patient and health professional can meet in a virtual environment (a virtual clinic). To this end, a prototype asynchronous telemedicine VR gait analysis system was designed, aiming to transfer a full clinical facility within the patients’ local proximity. The proposed system employs cost-effective alternative motion capture combined with the system’s immersive 3D virtual gait analysis clinic. The user interface and the tools in the application offer health professionals asynchronous, objective, and subjective analyses. This paper investigates the requirements for the design of such a system and discusses preliminary comparative data of its performance evaluation against a high-fidelity gait analysis clinical application

Exploring the Development Requirements for Virtual Reality Gait Analysis

The hip joint is highly prone to traumatic and degenerative pathologies resulting in irregular locomotion. Monitoring and treatment depend on high-end technology facilities requiring physician and patient co-location, thus limiting access to specialist monitoring and treatment for populations living in rural and remote locations. Telemedicine offers an alternative means of monitoring, negating the need for patient physical presence. In addition, emerging technologies, such as virtual reality (VR) and immersive technologies, offer potential future solutions through virtual presence, where the patient and health professional can meet in a virtual environment (a virtual clinic). To this end, a prototype asynchronous telemedicine VR gait analysis system was designed, aiming to transfer a full clinical facility within the patients’ local proximity. The proposed system employs cost-effective alternative motion capture combined with the system’s immersive 3D virtual gait analysis clinic. The user interface and the tools in the application offer health professionals asynchronous, objective, and subjective analyses. This paper investigates the requirements for the design of such a system and discusses preliminary comparative data of its performance evaluation against a high-fidelity gait analysis clinical application

Head orientation and gait stability in young adults, dancers and older adults

Background: Control of body orientation requires head motion detection by the vestibular system and small changes with respect to the gravitational acceleration vector could cause destabilization. Research question: We aimed to compare the effects of different head orientations on gait stability in young adults, dancers and older adults. Methods: Three groups of 10 subjects were evaluated, the first composed of young adults (aged 18–30 years), the second composed of young healthy dancers under high performance dance training (aged 18–30 years), and the third group composed of community-dwelling older adults (aged 65–80 years). Participants walked on a treadmill at their preferred speed in four distinct head orientation conditions for four minutes each: control (neutral orientation); dynamic yaw (following a target over 45° bilaterally); up (15° neck extension), and down (40° neck flexion). Foot and trunk kinematic data were acquired using a 3D motion capture system and the gait pattern was assessed by basic gait parameters (step length, stride width and corresponding variability) and gait stability (local divergence exponents and margins of stability). Main effects of conditions and groups, as well as their interaction effects, were evaluated by repeated-measures analysis of variance. Results: Interactions of group and head orientation were found for both step length and stride width variability; main effects of head orientation were found for all evaluated parameters and main effects of group were found for step length and its variability and local divergence exponents in all directions. Significance: As expected, the older adults group showed less stable gait (higher local divergence exponent), the shortest step length and greater step length variability. However, contrary to expectation, the dancers were not more stable. The yaw condition was the most challenging for all groups and the down condition seemed to be least challenging

Human Pose Detection for Robotic-Assisted and Rehabilitation Environments

Assistance and rehabilitation robotic platforms must have precise sensory systems for human–robot interaction. Therefore, human pose estimation is a current topic of research, especially for the safety of human–robot collaboration and the evaluation of human biomarkers. Within this field of research, the evaluation of the low-cost marker-less human pose estimators of OpenPose and Detectron 2 has received much attention for their diversity of applications, such as surveillance, sports, videogames, and assessment in human motor rehabilitation. This work aimed to evaluate and compare the angles in the elbow and shoulder joints estimated by OpenPose and Detectron 2 during four typical upper-limb rehabilitation exercises: elbow side flexion, elbow flexion, shoulder extension, and shoulder abduction. A setup of two Kinect 2 RGBD cameras was used to obtain the ground truth of the joint and skeleton estimations during the different exercises. Finally, we provided a numerical comparison (RMSE and MAE) among the angle measurements obtained with OpenPose, Detectron 2, and the ground truth. The results showed how OpenPose outperforms Detectron 2 in these types of applications.Óscar G. Hernández holds a grant from the Spanish Fundación Carolina, the University of Alicante, and the National Autonomous University of Honduras

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems