Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

EFFECTS OF LOCAL MUSCLE VIBRATION ON CARTILAGE DEFORMATION IN ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTED INDIVIDUALS ACUTELY AFTER WALKING

Objectives: To examine the effects of local muscle vibration (LMV) on cartilage deformation acutely after walking in individuals with anterior cruciate ligament reconstruction (ACLR). Design: Cross-over study. Participants: 12 ACLR individuals ages 18-35. Interventions: Participants performed isometric squats while being exposed to LMV or no vibration (control). Interventions were delivered in a counterbalanced design. Main outcome measures: Cartilage cross-sectional area (CSA) (mm2), quadriceps and hamstring co-activation. Results: Downward trends were noted in co-activation indices, at preparatory (P = 0.188), heelstrike (P = 0.148), and weight acceptance (P = 0.363) time points. No differences noted in cartilage strain, either between conditions, or within. No significant correlations noted between co-activation and cartilage deformation. Conclusions: These findings imply potential benefits to using LMV as an adjunct to traditional rehabilitation programs post ACLR. Future work is necessary to identify mechanisms by which co-activation is altered, as well as direct mechanisms of cartilage strain.Master of Art

동영상 속 사람 동작의 물리 기반 재구성 및 분석

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 이제희.In computer graphics, simulating and analyzing human movement have been interesting research topics started since the 1960s. Still, simulating realistic human movements in a 3D virtual world is a challenging task in computer graphics. In general, motion capture techniques have been used. Although the motion capture data guarantees realistic result and high-quality data, there is lots of equipment required to capture motion, and the process is complicated. Recently, 3D human pose estimation techniques from the 2D video are remarkably developed. Researchers in computer graphics and computer vision have attempted to reconstruct the various human motions from video data. However, existing methods can not robustly estimate dynamic actions and not work on videos filmed with a moving camera. In this thesis, we propose methods to reconstruct dynamic human motions from in-the-wild videos and to control the motions. First, we developed a framework to reconstruct motion from videos using prior physics knowledge. For dynamic motions such as backspin, the poses estimated by a state-of-the-art method are incomplete and include unreliable root trajectory or lack intermediate poses. We designed a reward function using poses and hints extracted from videos in the deep reinforcement learning controller and learned a policy to simultaneously reconstruct motion and control a virtual character. Second, we simulated figure skating movements in video. Skating sequences consist of fast and dynamic movements on ice, hindering the acquisition of motion data. Thus, we extracted 3D key poses from a video to then successfully replicate several figure skating movements using trajectory optimization and a deep reinforcement learning controller. Third, we devised an algorithm for gait analysis through video of patients with movement disorders. After acquiring the patients joint positions from 2D video processed by a deep learning network, the 3D absolute coordinates were estimated, and gait parameters such as gait velocity, cadence, and step length were calculated. Additionally, we analyzed the optimization criteria of human walking by using a 3D musculoskeletal humanoid model and physics-based simulation. For two criteria, namely, the minimization of muscle activation and joint torque, we compared simulation data with real human data for analysis. To demonstrate the effectiveness of the first two research topics, we verified the reconstruction of dynamic human motions from 2D videos using physics-based simulations. For the last two research topics, we evaluated our results with real human data.컴퓨터 그래픽스에서 인간의 움직임 시뮬레이션 및 분석은 1960 년대부터 다루어진 흥미로운 연구 주제이다. 몇 십년 동안 활발하게 연구되어 왔음에도 불구하고, 3차원 가상 공간 상에서 사실적인 인간의 움직임을 시뮬레이션하는 연구는 여전히 어렵고 도전적인 주제이다. 그동안 사람의 움직임 데이터를 얻기 위해서 모션 캡쳐 기술이 사용되어 왔다. 모션 캡처 데이터는 사실적인 결과와 고품질 데이터를 보장하지만 모션 캡쳐를 하기 위해서 필요한 장비들이 많고, 그 과정이 복잡하다. 최근에 2차원 영상으로부터 사람의 3차원 자세를 추정하는 연구들이 괄목할 만한 결과를 보여주고 있다. 이를 바탕으로 컴퓨터 그래픽스와 컴퓨터 비젼 분야의 연구자들은 비디오 데이터로부터 다양한 인간 동작을 재구성하려는 시도를 하고 있다. 그러나 기존의 방법들은 빠르고 다이나믹한 동작들은 안정적으로 추정하지 못하며 움직이는 카메라로 촬영한 비디오에 대해서는 작동하지 않는다. 본 논문에서는 비디오로부터 역동적인 인간 동작을 재구성하고 동작을 제어하는 방법을 제안한다. 먼저 사전 물리학 지식을 사용하여 비디오에서 모션을 재구성하는 프레임 워크를 제안한다. 공중제비와 같은 역동적인 동작들에 대해서 최신 연구 방법을 동원하여 추정된 자세들은 캐릭터의 궤적을 신뢰할 수 없거나 중간에 자세 추정에 실패하는 등 불완전하다. 우리는 심층강화학습 제어기에서 영상으로부터 추출한 포즈와 힌트를 활용하여 보상 함수를 설계하고 모션 재구성과 캐릭터 제어를 동시에 하는 정책을 학습하였다. 둘 째, 비디오에서 피겨 스케이팅 기술을 시뮬레이션한다. 피겨 스케이팅 기술들은 빙상에서 빠르고 역동적인 움직임으로 구성되어 있어 모션 데이터를 얻기가 까다롭다. 비디오에서 3차원 키 포즈를 추출하고 궤적 최적화 및 심층강화학습 제어기를 사용하여 여러 피겨 스케이팅 기술을 성공적으로 시연한다. 셋 째, 파킨슨 병이나 뇌성마비와 같은 질병으로 인하여 움직임 장애가 있는 환자의 보행을 분석하기 위한 알고리즘을 제안한다. 2차원 비디오로부터 딥러닝을 사용한 자세 추정기법을 사용하여 환자의 관절 위치를 얻어낸 다음, 3차원 절대 좌표를 얻어내어 이로부터 보폭, 보행 속도와 같은 보행 파라미터를 계산한다. 마지막으로, 근골격 인체 모델과 물리 시뮬레이션을 이용하여 인간 보행의 최적화 기준에 대해 탐구한다. 근육 활성도 최소화와 관절 돌림힘 최소화, 두 가지 기준에 대해 시뮬레이션한 후, 실제 사람 데이터와 비교하여 결과를 분석한다. 처음 두 개의 연구 주제의 효과를 입증하기 위해, 물리 시뮬레이션을 사용하여 이차원 비디오로부터 재구성한 여러 가지 역동적인 사람의 동작들을 재현한다. 나중 두 개의 연구 주제는 사람 데이터와의 비교 분석을 통하여 평가한다.1 Introduction 1 2 Background 9 2.1 Pose Estimation from 2D Video . . . . . . . . . . . . . . . . . . . . 9 2.2 Motion Reconstruction from Monocular Video . . . . . . . . . . . . 10 2.3 Physics-Based Character Simulation and Control . . . . . . . . . . . 12 2.4 Motion Reconstruction Leveraging Physics . . . . . . . . . . . . . . 13 2.5 Human Motion Control . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5.1 Figure Skating Simulation . . . . . . . . . . . . . . . . . . . 16 2.6 Objective Gait Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.7 Optimization for Human Movement Simulation . . . . . . . . . . . . 17 2.7.1 Stability Criteria . . . . . . . . . . . . . . . . . . . . . . . . 18 3 Human Dynamics from Monocular Video with Dynamic Camera Movements 19 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.3 Pose and Contact Estimation . . . . . . . . . . . . . . . . . . . . . . 21 3.4 Learning Human Dynamics . . . . . . . . . . . . . . . . . . . . . . . 24 3.4.1 Policy Learning . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4.2 Network Training . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4.3 Scene Estimator . . . . . . . . . . . . . . . . . . . . . . . . 29 3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.1 Video Clips . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.5.2 Comparison of Contact Estimators . . . . . . . . . . . . . . . 33 3.5.3 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.5.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Figure Skating Simulation from Video 42 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3 Skating Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3.1 Non-holonomic Constraints . . . . . . . . . . . . . . . . . . 46 4.3.2 Relaxation of Non-holonomic Constraints . . . . . . . . . . . 47 4.4 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.5 Trajectory Optimization and Control . . . . . . . . . . . . . . . . . . 50 4.5.1 Trajectory Optimization . . . . . . . . . . . . . . . . . . . . 50 4.5.2 Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5 Gait Analysis Using Pose Estimation Algorithm with 2D-video of Patients 61 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.2.1 Patients and video recording . . . . . . . . . . . . . . . . . . 63 5.2.2 Standard protocol approvals, registrations, and patient consents 66 5.2.3 3D Pose estimation from 2D video . . . . . . . . . . . . . . . 66 5.2.4 Gait parameter estimation . . . . . . . . . . . . . . . . . . . 67 5.2.5 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . 68 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.3.1 Validation of video-based analysis of the gait . . . . . . . . . 68 5.3.2 gait analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.4.1 Validation with the conventional sensor-based method . . . . 75 5.4.2 Analysis of gait and turning in TUG . . . . . . . . . . . . . . 75 5.4.3 Correlation with clinical parameters . . . . . . . . . . . . . . 76 5.4.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.5 Supplementary Material . . . . . . . . . . . . . . . . . . . . . . . . . 77 6 Control Optimization of Human Walking 80 6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.2.1 Musculoskeletal model . . . . . . . . . . . . . . . . . . . . . 82 6.2.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.2.3 Control co-activation level . . . . . . . . . . . . . . . . . . . 83 6.2.4 Push-recovery experiment . . . . . . . . . . . . . . . . . . . 84 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7 Conclusion 90 7.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Docto

Analysis of Affective State as Covariate in Human Gait Identification

There is an increased interest in the need for a noninvasive and nonintrusive biometric identification and recognition system such as Automatic Gait Identification (AGI) due to the rise in crime rates in the US, physical assaults, and global terrorism in public places. AGI, a biometric system based on human gait, can recognize people from a distance and current literature shows that AGI has a 95.75% success rate in a closely controlled laboratory environment. Also, this success rate does not take into consideration the effect of covariate factors such as affective state (mood state); and literature shows that there is a lack of understanding of the effect of affective state on gait biometrics. The purpose of this study was to determine the percent success rate of AGI in an uncontrolled outdoor environment with affective state as the main variable. Affective state was measured using the Profile of Mood State (POMS) scales. Other covariate factors such as footwear or clothes were not considered in this study. The theoretical framework that grounded this study was Murray\u27s theory of total walking cycle. This study included the gait signature of 24 participants from a population of 62 individuals, sampled based on simple random sampling. This quantitative research used empirical methods and a Fourier Series Analysis. Results showed that AGI has a 75% percent success rate in an uncontrolled outdoor environment with affective state. This study contributes to social change by enhancing an understanding of the effect of affective state on gait biometrics for positive identification during and after a crime such as bank robbery when the use of facial identification from a surveillance camera is either not clear or not possible. This may also be used in other countries to detect suicide bombers from a distance

Computational Tools and Experimental Methods for the Development of Passive Prosthetic Feet

Modern prosthetic foot designs are incredibly diverse in comparison to what was o↵ered to amputees at the turn of the millennium. Powered ankles can supply natural levels of joint torque, whilst passive feet continue to optimise for kinematic goals. However, most passive feet still do not solve the issue of unhealthy loads, and an argument can be made that optimisation methods have neglected the less active and elderly amputee. This thesis creates a framework for a novel approach to prosthetic foot optimisation by focusing on the transitionary motor tasks of gait initiation and termination.An advanced FEA model has been created in ANSYS® using boundary con-ditions derived from an ISO testing standard that replicates stance phase loading. This model can output standard results found in the literature and goes beyond by parameterising the roll-over shape within the software using custom APDL code. Extensive contact exploration and an experimental study have ensured the robustness of the model. Subject force and kinematic data can be used for specific boundary conditions, which would allow for easy adaptation to the transitionary motor tasks.This FEA model has been used in the development of prosthetic experiment tool, which can exchange helical springs to assess e↵ects of small changes in sti↵-ness on gait metrics. A rigorous design methodology was employed for all compo-nents, including parametric design studies, response surface optimisation, and ISO level calculations. The design has been manufactured into a working prototype and is ready for clinical trials to determine its efficacy.The conclusion of this framework is in the development of an experimental method to collect subject data for use in the models. A pilot study uncovered reliable protocols, which were then verified with ANOVA statistics. Proportional ratios were defined as additions to metric peak analyses already found in the liter-ature. These tools are ready for deployment in full clinical trials with amputees, so that a new prosthetic optimisation pathway can be discovered for the benefit of less active or elderly amputees

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201