Body Motion Capture Research Articles

A Motion Capture Dataset on Human Sitting to Walking Transitions

Sit-to-walk (STW) is a crucial daily task that impacts mobility, independence, and thus quality of life. Existing repositories have limited STW data with small sample sizes (n = 10). Hence, this study presents a STW dataset obtained via the time-up-and-go test, for 65 healthy adults across three age groups – young (19–35 years), middle (36–55 years) and older (above 56 years). The dataset contains lower body motion capture, ground reaction force, surface electromyography, inertial measurement unit data, and responses for the knee injury and osteoarthritis outcome score survey. For validation, the within subjects intraclass correlation coefficients for the maximum and minimum lower body joint angles were calculated with values greater than 0.74, indicating good test-retest reliability. The joint angle trajectories and maximum voluntary contractions are comparable with existing literature, matching in overall trends and range. Accordingly, this dataset allows STW biomechanics, executions, and characteristics to be studied across age groups. Biomechanical trajectories of healthy adults serve as a benchmark in assessing neuromusculoskeletal impairments and when designing assistive technology for treatment or rehabilitation.

Research on the Application of Virtual Reality Technology in Motor Skills Training

Abstract The emerging efficacy of Virtual Reality (VR) technologies in sports training is explored in this study. We developed a VR-based motor skill training model utilizing a markerless optical motion capture system. This system employs a domain adaptive detection method in the 3D human posture realm, enabling precise identification of motor action postural feature points in students. Subsequently, the model incorporates a human dynamic motion tracking function that minimizes computational errors in motion capture data, facilitating the implementation of multi-feature fusion-based virtual digital human body motion capture technology. An empirical analysis was conducted using H College University as the case study to evaluate the effectiveness of virtual motor skill training. The findings demonstrate that the average accuracy of the proposed method for recognizing body gestures achieved a significant level of 0.834, enabling more accurate identification of students’ motor action characteristics. Moreover, the post-test results of the VR class showed marked improvements, ranging from 0.56 to 15.56 points over traditional lecture classes and 0.43 to 10.33 points over video teaching classes. These substantial variances not only underline the superiority of the VR-based training model in enhancing academic interest and physical fitness but also in advancing motor skills among learners. The study accentuates VR’s potential to augment motor skills and foresees its expansive application in future motor training domains.

Closed-Loop FES Control of a Hybrid Exoskeleton during Sit-to-Stand Exercises: Concept and First Evaluation

Rehabilitation of paralysis caused by a stroke or a spinal cord injury remains a complex and time-consuming task. This work proposes a hybrid exoskeleton approach combining a traditional exoskeleton and functional electrical stimulation (FES) as a promising method in rehabilitation. However, hybrid exoskeletons with a closed-loop FES control strategy are functionally challenging to achieve and have not been reported often. Therefore, this study aimed to investigate a powered lower-limb exoskeleton with a closed-loop FES control for Sit-to-Stand (STS) movements. A body motion capture system was applied to record precise hip and knee trajectories of references for establishing the human model. A closed-loop control strategy with allocation factors is proposed featuring a two-layer cascaded proportional–integral–derivative (PID) controller for both FES and exoskeleton control. Experiments were performed on two participants to examine the feasibility of the hybrid exoskeleton and the closed-loop FES control. Both open- and closed-loop FES control showed the desired performance with a relatively low root-mean-squared error (max 1.3∘ in open-loop and max 4.1∘ in closed-loop) in hip and knee trajectories. Notably, the closed-loop FES control strategy can achieve the same performance with nearly 60% of the electrical power input compared to the open-loop control, which reduced muscle fatigue and improved robustness during the training. This study provides novel insights into body motion capture application and proposes a closed-loop FES control for hybrid exoskeletons.

Patellar tendon elastic properties derived from in vivo loading and kinematics

Patellar complications frequently limit the success of total knee arthroplasty. In addition to the musculoskeletal forces themselves, patellar tendon elastic properties are essential for driving patellar loading. Elastic properties reported in the literature exhibit high variability and appear to differ according to the methodologies used. Specifically in total knee arthroplasty patients, only limited knowledge exists on in vivo elastic properties and their corresponding loads. For the first time, we report stiffness, Young’s modulus, and forces of the patellar tendon, derived from four patients with telemetric total knee arthroplasties using a combined imaging and measurement approach. To achieve this, synchronous in vivo telemetric assessment of tibio-femoral contact forces and fluoroscopic assessment of knee kinematics, along with full body motion capture and ground reaction forces, fed musculoskeletal multi-body models to quantify patellar tendon loading and elongation. Mechanical patellar tendon properties were calculated during a squat and a sit-stand-sit activity, with resulting tendon stiffness and Young’s modulus ranging from 511 to 1166 N/mm and 259 to 504 MPa, respectively. During these activities, the patellar tendon force reached peak values between 1.31 and 2.79 bodyweight, reaching levels of just ∼0.5 bodyweight below the tibio-femoral forces. The results of this study provide valuable input data for mechanical simulations of the patellar tendon and the whole resurfaced knee.

Review of Optical and Inertial Technologies for Lower Body Motion Capture

Review of Optical and Inertial Technologies for Lower Body Motion Capture

Meta-Interface Analysis for Interaction in Mobile Augmented Reality

Mobile Augmented Reality has expanded including the cultural heritage sector. Among the options offered is the digitization of cultural heritage. Multiple technical breakthroughs has implemented, including hand gesture recognition and body tracking, into mobile augmented reality systems. The success of these improvements depends on a good user interface. Meta User Interface (Meta-UI) allows significant information to be displayed without interfering with system operation. The results of the analyses that the Meta-UI user interface could aid users in employing hand gestures and body motion capture on this mobile augmented reality without any issues and could deliver information about batik efficiently. PIECES Framework testing confirmed this, with a satisfaction score of 4.40 to 4.55. Using the PIECES Framework concepts and questionnaire that focus on user satisfaction with mobile augmented reality, the score indicates that the user is satisfied.

Intrinsic factors contributing to elevated intra-abdominal pressure

Pelvic floor disorders affect 24% of US women, and elevated intra-abdominal pressure may cause pelvic injury through musculoskeletal strain. Activity restrictions meant to reduce pelvic strain after traumatic events, such as childbirth, have shown little benefit to patients. Reported high variability in abdominal pressure suggests that technique plays a substantial role in pressure generation. Understanding these techniques could inform evidence-based recommendations for protective pelvic care. We hypothesized use of a motion-capture methodology could identify four major contributors to elevated pressure: gravity, acceleration, abdominal muscle contraction, and respiration. Twelve women completed nineteen activities while instrumented for whole body motion capture, abdominal pressure, hip acceleration, and respiration volume. Correlation and partial least squares regression were utilized to determine primary technique factors that increase abdominal pressure. The partial least squares model identified two principal components that explained 59.63% of relative intra-abdominal pressure variability. The first component was primarily loaded by hip acceleration and relative respiration volume, and the second component was primarily loaded by flexion moments of the abdomen and thorax. While reducing abdominal muscle use has been a primary strategy in protective pelvic floor care, the influence of hip acceleration and breathing patterns should be considered with similar importance in future work.

Wearable biosensors for real-time sweat analysis and body motion capture based on stretchable fiber-based triboelectric nanogenerators

Carbon neutrality is a global green energy revolution meaning that the carbon dioxide can make ends meet. However, with the mushroom of the fifth generation wireless systems (5G) and the Internet of Things (IoT), it is a great challenge for powering the ubiquitous distributed devices, because the battery production and high overhead maintenance may bring more carbon emissions. Here, we present wearable biosensors for real-time sweat analysis and body motion capture based on stretchable fiber-based triboelectric nanogenerators (F-TENG). The F-TENG is made of stretchable conductive fiber (Ecoflex coating with polyaniline (PANI)) and varnished wires. Based on the coupling effect of triboelectric effect and enzymatic reaction (surface-triboelectric coupling effect), the wearable biosensors can not only precisely sense the motion states, but also detect glucose, creatinine and lactate acid in sweat in real-time. Importantly, the wearable devices can self-drive without any external power source and the response against glucose, creatinine and lactate acid can be up to 103%, 125% and 38%, respectively. On this basis, applications in biosensing and wireless communication have been demonstrated. This work exhibits a prospective potential application of F-TENG in IoT for diverse use.

Realistic Motion Avatars are the Future for Social Interaction in Virtual Reality

This study evaluated participant self-reported appraisal of social interactions with another person in virtual reality (VR) where their conversational partner was represented by a realistic motion avatar. We use the term realistic motion avatar because: 1. The avatar was modelled to look like the conversational partner it represented, and 2. Full face and body motion capture was utilised so that the avatar mimicked the facial and body language of the conversational partner in real-time. We compared social interaction in VR with face-to-face interaction across two communicative contexts: 1. Getting acquainted conversation, and 2. A structured interview where the participant engaged in self-disclosure about positive and negative experiences. Overall, participants largely indicated they preferred face-to-face over VR communication. However, some participants did indicate a preference for VR communication. Additionally, an analysis of post-conversation ratings indicated no significant difference for rated enjoyment, understanding, self-disclosure, comfort, and awkwardness between communication modes. The only ratings where face-to-face was found to be superior was for perceived closeness across both types of communication, and for feeling understood specifically when disclosing negative experiences. Most participants perceived frequent eye contact in both face-to-face and VR interaction, but typically more eye contact when face-to-face. Eye contact was positively associated with rated enjoyment, closeness, and comfort. Overall, our findings suggest that harnessing full face and body motion capture can make social interaction in VR very similar to face-to-face interaction. We anticipate that VR social interaction is poised to become the next major technological evolution for human computer mediated communication and suggest avenues for further research.

A Pilot Study Quantifying Center of Mass Trajectory during Dynamic Balance Tasks Using an HTC Vive Tracker Fixed to the Pelvis.

Fall rates are increasing among the aging population and even higher falls rates have been reported in populations with neurological impairments. The Berg Balance Scale is often used to assess balance in older adults and has been validated for use in people with stroke, traumatic brain injury, and Parkinson’s disease. While the Berg Balance Scale (BBS) has been found to be predictive of the length of rehabilitation stay following stroke, a recent review concluded the BBS lacked predictive validity for fall risk. Conversely, sophisticated measures assessing center of mass (COM) displacement have shown to be predictive of falls risk. However, calculating COM displacement is difficult to measure outside a laboratory. Accordingly, we sought to validate COM displacement measurements derived from an HTC Vive tracker secured to the pelvis by comparing it to COM derived from ‘gold’ standard laboratory-based full-body motion capture. Results showed that RMS between the COM calculated from HTC Vive tracker and full body motion capture agree with an average error rate of 2.1 ± 2.6 cm. Therefore, we conclude measurement of COM displacement using an HTC Vive tracker placed on the pelvis is reasonably representative of laboratory-based measurement of COM displacement.

Monitoring dynamic breast measurements obtained from 4D body scanning

PurposeThe purpose of this study is to investigate the shape change of breast during movement to inform product development of bras and other female wearable products.Design/methodology/approachUsing the latest 4D body scanning technology, the authors monitored the change of seven non-circumferential breast measurements, including four linear measurements (widths, depth, etc.) and three angular measurements, across nine dynamic scans of a complete gait cycle during running. A series of statistical analysis were conducted to thoroughly investigate the measurement values in dynamic states compared with values extracted from static 3D scans.FindingsMajor findings are as follows: (1) For width-underbust, chest-depth, vertical-acromion and angle ABD, more than half of the dynamic frames presents a significant difference with the static frame. (2) Width-underbust and chest-depth measured in static can underestimate the actual values under motion. (3) vertical-acromion presents a W-shaped general trend for the nine dynamic frames with peaks observed at the keyframes (i.e. when a participant's right or left knee bends the most and rises to its highest level) and lows at the intermediate frames. (4) Angle ABD and angle BAD both present an M-shaped general trend, the exact opposite of a W-shaped trend.Originality/valueWhile 3D body scanning and motion capture systems have both contributed significantly to the study of breast, 4D body scanning incorporates the advantages of both technologies and captures the 3D surface of the body during movement at each instant moment. This is one of the first studies that adopt the new technology for apparel applications.

Fall detecting clothes in realtime based seniors full body motion capture system using multiple inertial sensors

The physical ability of the elderly will decrease along with the aging process, thus increasing the potential for falls. An elderly person who has experienced a fall and is not detected for a long time will bring many possible consequences. Early detection of an elderly fall will help to minimize this possibility by reducing the time between the occurrence of an event and the arrival of medical assistance The purpose of this research is to build a Fall Detecting Clothes in Realtime Based Art Full Body Motion Capture System Using Multiple Inertial Sensors. This system builds by Waterfall methodology where every research step is done in sequence, start from analysis, design, coding, testing. Testing methodology is black box, which is a test every feature function. The Fall Detecting Clothes in Real Time Based Systems Full Body Motion Capture System Using Multiple Inertial Sensors has been tested using black testing box with the results of all feature functions working properly. The system has been tested with several conditions with the results of all controls and output data works properly as well.

A Magnetometer Calibration Method Using Single-Axis Motion Trajectory and Unscented Kalman Filter for Body Motion Capture Applications

Magnetometer calibration is a pre-processing step in the Attitude and Heading Reference Systems (AHRS) which has an essential role in many applications. The main purpose of this article is to derive an innovative and precise calibration approach for a magnetometer set installed on human body. To execute this calibration method, all the error parameters of multiple magnetometers are considered in an Unscented Kalman Filter (UKF) model for accurate estimation of calibration parameters. As achieving a precise estimation in Kalman filters-based algorithms needs an accurate and complete observation model, a special single-axis rotation trajectory for Inertial Measurement Unit (IMU) is performed to increase the observability rank of the calibration model. To evaluate the proposed method, five body-mounted sensors were experimented in the laboratory at the same time for applying in the body motion capture system. The results showed that all five sensors were well-calibrated without any need to be detached from the body and using any rotational robot arm. The resolution and precision of the proposed calibration method are assessed by the ellipsoid-fitting representation method. Consequently, all the body-mounted magnetometers were calibrated, on average, by about 1% uncertainty. The method can be used in every motion capture and AHRS applications due to its feasibility and simplicity.

Development and fabrication of highly flexible, stretchable, and sensitive strain sensor for long durability based on silver nanoparticles–polydimethylsiloxane composite

In this study, silver nanoparticles (AgNPs) conductive filler was attached on polydimethylsiloxane (PDMS) substrate to produce resistive-type strain sensors. The sandwich-structured-like PDMS covered AgNPs strain sensors (Ag-PDMS) and AgNPs modified PDMS composite strain sensors (Ag@PDMS) were fabricated by a drop-casting method. The effect of AgNPs loading such as 0.20 wt%, 0.25 wt%, and 0.30 wt% with PDMS was investigated and electromechanical performance were compared between Ag-PDMS and Ag@PDMS composite strain sensors. Based on the strain performance, the gauge factor of strain sensor fabricated with AgNPs loading with 0.25 wt% in PDMS (Ag0.25@PDMS) composite revealed the gauge factor of 10.08 with a strain range of 70%, which demonstrated fast time response, good stretchability, durability, and repeatability. Ag0.25@PDMS composite strain sensor fabricated in the present study is able to be used in finger motion detection, which indicates the capability of the sensor to be used for human body motion capture and other highly sensitive strain-sensing applications.

Development of a Biomechatronic Device for Motion Analysis Through a RGB-D Camera

This work investigates the validity and reliability of a novel biomechatronic device providing an interactive environment in Augmented Reality (AR) for neuromotor rehabilitation. A RGB-depth camera and telemonitoring/remote signaling module are the main components of the device, together with a PC-based interface. The interactive environment, which implements some optimized algorithms of body motion capture and novel methodologies for human body motion analysis, enables neuromotor rehabilitation treatments that are adaptable to the performance and individual characteristics of the patient. The RGB-Depth camera module is implemented through a Microsoft Kinect, ORBBEC ZED2K devices; the telemonitoring module for teleassistance and therapy supervision is implemented as a cloud service.
 Within the module of body motion tracking, the abduction and adduction movements of the limbs of the full-body structure are tracked and the joints angles are measured in real-time; the most distinctive feature of the tracking module is the control of the trunk and shoulder posture during the exercises performed by the patient. Indeed, the device recognizes an incorrect position of the patient's body that could affect the objective of the exercise to be performed. The recognition of an incorrect exercise is associated to the generation of an alert both to the patient and the physician, in order to maximize the effectiveness of the treatment based on the user's potential and to increase the chances of a better biofeedback.
 The experimental tests, which have been carried out by reproducing several neuromotor exercises on the interactive environment, show that the feature recognition and extraction of the joints and segments of the musculo-skeletal structure of the patient's, and of wrong posture during exercises, can achieve good performance in the different experimental conditions. 
 The developed device is a valid tool for patients affected by chronic disability, but it could be extended to neurodegenerative diseases in the early stages of the disease. Thanks to the enhanced interactivity in augmented reality, the patient can overcome some difficulties in interaction with the most common IT tools and technologies; at the meanwhile she/he can perform rehabilitation at home. The physician can also check in real time the results and customize the care pathway.
 The enhanced interactivity provided by the device during rehabilitation session increases both the motivation by the patient and the continuity of the care, as well as it supports low-cost remote assistance and telemedicine by optimizing therapy costs.
 The key points are:
 
 i) making rehabilitation motivating for the patient, becoming a "player";
 ii) optimize effectiveness and costs;
 
 iii) possibility of low-cost remote assistance and telemedicine.

Merging fNIRS-EEG Brain Monitoring and Body Motion Capture to Distinguish Parkinsons Disease.

Functional connectivity between the brain and body kinematics has largely not been investigated due to the requirement of motionlessness in neuroimaging techniques such as functional magnetic resonance imaging (fMRI). However, this connectivity is disrupted in many neurodegenerative disorders, including Parkinsons Disease (PD), a neurological progressive disorder characterized by movement symptoms including slowness of movement, stiffness, tremors at rest, and walking and standing instability. In this study, brain activity is recorded through functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and body kinematics were captured by a motion capture system (Mocap) based on an inertial measurement unit (IMU) for gross movements (large movements such as limb kinematics), and the WearUp glove for fine movements (small range movements such as finger kinematics). PD and neurotypical (NT) participants were recruited to perform 8 different movement tasks. The recorded data from each modality have been analyzed individually, and the processed data has been used for classification between the PD and NT groups. The average changes in oxygenated hemoglobin (HbO2) from fNIRS, EEG power spectral density in the Theta, Alpha, and Beta bands, acceleration vector from Mocap, and normalized WearUp flex sensor data were used for classification. 12 different support vector machine (SVM) classifiers have been used on different datasets such as only fNIRS data, only EEG data, hybrid fNIRS/EEG data, and all the fused data for two classification scenarios: classifying PD and NT based on individual activities, and all activity data fused together. The PD and NT group could be distinguished with more than 83% accuracy for each individual activity. For all the fused data, the PD and NT groups are classified with 81.23%, 92.79%, 92.27%, and 93.40% accuracy for the fNIRS only, EEG only, hybrid fNIRS/EEG, and all fused data, respectively. The results indicate that the overall performance of classification in distinguishing PD and NT groups improves when using both brain and body data.

Multiple instance deep learning for weakly-supervised visual object tracking

Intelligently tracking objects with varied shapes, color, lighting conditions, and backgrounds is an extremely useful application in many HCI applications, such as human body motion capture, hand gesture recognition, and virtual reality (VR) games. However, accurately tracking different objects under uncontrolled environments is a tough challenge due to the possibly dynamic object parts, varied lighting conditions, and sophisticated backgrounds. In this work, we propose a novel semantically-aware object tracking framework, wherein the key is weakly-supervised learning paradigm that optimally transfers the video-level semantic tags into various regions. More specifically, give a set of training video clips, each of which is associated with multiple video-level semantic tags, we first propose a weakly-supervised learning algorithm to transfer the semantic tags into various video regions. The key is a MIL (Zhong et al., 2020) [1]-based manifold embedding algorithm that maps the entire video regions into a semantic space, wherein the video-level semantic tags are well encoded. Afterward, for each video region, we use the semantic feature combined with the appearance feature as its representation. We designed a multi-view learning algorithm to optimally fuse the above two types of features. Based on the fused feature, we learn a probabilistic Gaussian mixture model to predict the target probability of each candidate window, where the window with the maximal probability is output as the tracking result. Comprehensive comparative results on a challenging pedestrian tracking task as well as the human hand gesture recognition have demonstrated the effectiveness of our method. Moreover, visualized tracking results have shown that non-rigid objects with moderate occlusions can be well localized by our method.

Effect of preferred walking speed on the upper body range of motion and mechanical work during gait before and after spinal fusion for patients with idiopathic scoliosis

BackgroundScoliosis may have an effect on gait parameters, the kinematics of the lower limbs and the spine, and mechanical work with specific gait speed. Imposed gait speed may influence these effects. Following spinal fusion in the case of idiopathic scoliosis, patients fear subsequent and considerable back stiffness and kinetic consequences. The aim of this retrospective study was to evaluate the upper body range of motion and mechanical work before and after spinal fusion in of free gait speed conditions. MethodsTwenty-two patients with idiopathic scoliosis and twenty-two asymptomatic controls were included. Patients were analyzed before and one year after spinal fusion. Based on full body modeling and motion capture, we measured gait speed, cadence, stride length, the mobility of the upper and lower spinal segments (in each plane), and mechanical work (with and without dimensionless scaling strategy). FindingsPatients walked significantly slower than controls. The same speed was noticed before and after fusion. Only the lower back kinematics in the frontal plane was reduced before fusion. Spinal fusion further reduced the mobility of the pelvis segment in the sagittal plane in comparison to controls. Scaling external work was associated with higher values for patients. InterpretationSpinal fusion improves pelvic and thorax-pelvis mobility (during the stance phase) in the frontal plane. The impact of scoliosis on the upper body range of motion was limited on the thorax-pelvis, corresponding to a stiffening effect. With such restrictions, an increase in normalized external work was observed for similar normalized internal work.

Introducing ACASS: An Annotated Character Animation Stimulus Set for Controlled (e)Motion Perception Studies.

Others' movements inform us about their current activities as well as their intentions and emotions. Research on the distinct mechanisms underlying action recognition and emotion inferences has been limited due to a lack of suitable comparative stimulus material. Problematic confounds can derive from low-level physical features (e.g., luminance), as well as from higher-level psychological features (e.g., stimulus difficulty). Here we present a standardized stimulus dataset, which allows to address both action and emotion recognition with identical stimuli. The stimulus set consists of 792 computer animations with a neutral avatar based on full body motion capture protocols. Motion capture was performed on 22 human volunteers, instructed to perform six everyday activities (mopping, sweeping, painting with a roller, painting with a brush, wiping, sanding) in three different moods (angry, happy, sad). Five-second clips of each motion protocol were rendered into AVI-files using two virtual camera perspectives for each clip. In contrast to video stimuli, the computer animations allowed to standardize the physical appearance of the avatar and to control lighting and coloring conditions, thus reducing the stimulus variation to mere movement. To control for low level optical features of the stimuli, we developed and applied a set of MATLAB routines extracting basic physical features of the stimuli, including average background-foreground proportion and frame-by-frame pixel change dynamics. This information was used to identify outliers and to homogenize the stimuli across action and emotion categories. This led to a smaller stimulus subset (n = 83 animations within the 792 clip database) which only contained two different actions (mopping, sweeping) and two different moods (angry, happy). To further homogenize this stimulus subset with regard to psychological criteria we conducted an online observer study (N = 112 participants) to assess the recognition rates for actions and moods, which led to a final sub-selection of 32 clips (eight per category) within the database. The ACASS database and its subsets provide unique opportunities for research applications in social psychology, social neuroscience, and applied clinical studies on communication disorders. All 792 AVI-files, selected subsets, MATLAB code, annotations, and motion capture data (FBX-files) are available online.

Lower limb sagittal kinematic and kinetic modeling of very slow walking for gait trajectory scaling.

Lower extremity powered exoskeletons (LEPE) are an emerging technology that assists people with lower-limb paralysis. LEPE for people with complete spinal cord injury walk at very slow speeds, below 0.5m/s. For the able-bodied population, very slow walking uses different neuromuscular, locomotor, postural, and dynamic balance control. Speed dependent kinetic and kinematic regression equations in the literature could be used for very slow walking LEPE trajectory scaling; however, kinematic and kinetic information at walking speeds below 0.5 m/s is lacking. Scaling LEPE trajectories using current reference equations may be inaccurate because these equations were produced from faster than real-world LEPE walking speeds. An improved understanding of how able-bodied people biomechanically adapt to very slow walking will provide LEPE developers with more accurate models to predict and scale LEPE gait trajectories. Full body motion capture data were collected from 30 healthy adults while walking on an instrumented self-paced treadmill, within a CAREN-Extended virtual reality environment. Kinematic and kinetic data were collected for 0.2 m/s—0.8 m/s, and self-selected walking speed. Thirty-three common sagittal kinematic and kinetic gait parameters were identified from motion capture data and inverse dynamics. Gait parameter relationships to walking speed, cadence, and stride length were determined with linear and quadratic (second and third order) regression. For parameters with a non-linear relationship with speed, cadence, or stride-length, linear regressions were used to determine if a consistent inflection occurred for faster and slower walking speeds. Group mean equations were applied to each participant’s data to determine the best performing equations for calculating important peak sagittal kinematic and kinetic gait parameters. Quadratic models based on walking speed had the strongest correlations with sagittal kinematic and kinetic gait parameters, with kinetic parameters having the better results. The lack of a consistent inflection point indicated that the kinematic and kinetic gait strategies did not change at very slow gait speeds. This research showed stronger associations with speed and gait parameters then previous studies, and provided more accurate regression equations for gait parameters at very slow walking speeds that can be used for LEPE joint trajectory development.