MotionAnalytics Overview
MotionAnalytics is advancing biomechanical AI by applying over 20 years of biomechanics research from Ben-Gurion University. Its proprietary LBM (Large Biomechanics Models) technology offers a 10x improvement in movement analysis precision, providing real-time insights and biomechanical simulation-based generative AI. LBM powers MotionAnalytics' flagship product, VTrainer, an AI-driven tool that automates video analysis for online coaches, offering real-time feedback on form, injury prevention alerts, and personalized biomechanics-based training. The company’s initial module, SwiMe, serves swim coaches and demonstrates LBM's versatility in analyzing challenging movements. VTrainer aims to expand into broader fitness markets, supporting coaches and fitness professionals in the $54 billion digital fitness industry. With a SaaS subscription model for B2B users, MotionAnalytics plans to grow by offering LBM as a Platform-as-a-Service for various sectors, including sports tech, healthcare, and security, promoting real-time movement analysis and performance optimization across industries.
Latest News
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Metabolic power response to added mass on the lower extremities during running
The study explores the metabolic cost of running with additional leg mass using wearable exoskeletal devices. 15 participants were made to run on a treadmill with added mass on different parts of their legs. The study found that adding mass more distally on the leg increases the metabolic cost of running to a greater extent. For the same absolute added mass on the foot or shank, metabolic power increases more in females.
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Joint kinematic and kinetic responses to added mass on the lower extremities during running
The study analyzed the biomechanical response to running with added leg mass, which could inform the design of wearable locomotor assistive devices such as exoskeletons. 15 participants completed treadmill running trials with lead mass attached to the thigh, shank, or foot. The results showed moderate kinematic changes in response to the distal added limb mass. Adding even small masses made significant changes to the joint moments and powers, mostly during the swing phase. These findings have implications for shoe designs, people who run with added mass on their legs for sport/strength training, and for the design of wearable devices.
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Emotion and motion: Toward emotion recognition based on standing and walking
Researchers have developed an approach for emotion recognition based on body motion in naturalistic settings. The study examined authentic emotions, natural movement, and a broad collection of motion parameters. The researchers manipulated participants’ emotions using pretested movies into five conditions: happiness, relaxation, fear, sadness, and emotionally-neutral. A motion capture system measured posture and motion during standing and walking. The study found that it is impossible to recognize emotions based on a single motion parameter. However, machine learning models were developed to classify emotions using a collection of parameters. The decision tree using 25 parameters provided the highest average accuracy.
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Parametric equations to study and predict lower-limb joint kinematics and kinetics during human walking and slow running on slopes
A study presents an empirical mathematical model that predicts lower-limb joint kinematics and kinetics during human walking and running as a function of surface gradient and stride cycle percentage. The study involved 9 males and 7 females who walked and ran at different speeds and surface gradients. The data collected was used to generate prediction equations for each speeds slope. The equations could be used in the design of exoskeletons for walking and running on slopes or for educational purposes in gait studies.
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Effect of using real motion versus predicted motion as input for digital human modeling of back and shoulder loads during manual material handling
The study evaluates the differences in L4/L5 compression force and shoulder torques during a work process calculated using Digital Human Modeling (DHM) with motion prediction (Jack by Siemens) and DHM with experimental data. The work process is a sequential removing, carrying, and depositing task performed by nine females and nine males and recorded using a motion capture system. The analysis shows that using experimental data results in larger back compression force during the removing task, similar force during the depositing task, and less force during the carrying task.
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| Sector | Business Software |
Funding
| Total funding | $150K |
| Last funding | $150K |
| Stage | Pre-Funding |
| Rounds | 1 |
| Investors | 0 |
Team Members
3
Employees: 1-10
Web & Social Links
| Website | www.motionanalytics.io/ |
Locations
Merkaz Ba'alei Melaha, Tel Aviv-Yafo, Israel
Photos & Videos
3 item(s)
MotionAnalytics Business
Business models
B2B
Product stage
Beta
Employees
1-10
Sector
Business SoftwareIT, R&D & Data SolutionsData Analysis & Decision SupportTarget Customer
Enterprise & Professional ServicesService ProvidersEnterprisesSoftware DevelopmentDefense, Safety & SecuritySecurityHomeland SecuritySafetyConsumersRecreational & LifestyleFitness & SportsHealthcare & Life SciencesHealthcarePatientsProvidersInsurance CompaniesFinancial ServicesFinancial ProductsInsuranceMedia & EntertainmentSportsGaming IndustryGovernment & CityCore Technology
Platforms & InterfacesMobileAPIWebSensingMotionBiometricsArtificial IntelligenceMachine LearningDeep LearningImage RecognitionGenerative AIComputer VisionSimulation & ImagingTags (30)
athletesremote-sensingcloud-platformemotions-analyticstrainingsportscomputer-visionsports-techartificial-intelligencemachine-learningbiosensorsearly-detectioncontent-recommendationcloud-basedbiometricsanalysis-softwaremachine-visionrecommendationsweb-applicationidentity-verificationpreventionsaasmotion-trackingfall-preventionpredictive-analyticspersonalizationmotion-detectionsimulationmobile-applicationshuman-machine-interfaceGeographic Markets
North AmericaEuropeIsraelMotionAnalytics Financials
$150K
Total funding
—
Capital raised
$150K
Last funding
Pre-Funding
Funding stage
1
Total rounds
0
Investors
MotionAnalytics Lifecycle
MotionAnalytics News
14 articles
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Metabolic power response to added mass on the lower extremities during running
The study explores the metabolic cost of running with additional leg mass using wearable exoskeletal devices. 15 participants were made to run on a treadmill with added mass on different parts of their legs. The study found that adding mass more distally on the leg increases the metabolic cost of running to a greater extent. For the same absolute added mass on the foot or shank, metabolic power increases more in females.
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Joint kinematic and kinetic responses to added mass on the lower extremities during running
The study analyzed the biomechanical response to running with added leg mass, which could inform the design of wearable locomotor assistive devices such as exoskeletons. 15 participants completed treadmill running trials with lead mass attached to the thigh, shank, or foot. The results showed moderate kinematic changes in response to the distal added limb mass. Adding even small masses made significant changes to the joint moments and powers, mostly during the swing phase. These findings have implications for shoe designs, people who run with added mass on their legs for sport/strength training, and for the design of wearable devices.
Not applicable
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Emotion and motion: Toward emotion recognition based on standing and walking
Researchers have developed an approach for emotion recognition based on body motion in naturalistic settings. The study examined authentic emotions, natural movement, and a broad collection of motion parameters. The researchers manipulated participants’ emotions using pretested movies into five conditions: happiness, relaxation, fear, sadness, and emotionally-neutral. A motion capture system measured posture and motion during standing and walking. The study found that it is impossible to recognize emotions based on a single motion parameter. However, machine learning models were developed to classify emotions using a collection of parameters. The decision tree using 25 parameters provided the highest average accuracy.
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Parametric equations to study and predict lower-limb joint kinematics and kinetics during human walking and slow running on slopes
A study presents an empirical mathematical model that predicts lower-limb joint kinematics and kinetics during human walking and running as a function of surface gradient and stride cycle percentage. The study involved 9 males and 7 females who walked and ran at different speeds and surface gradients. The data collected was used to generate prediction equations for each speeds slope. The equations could be used in the design of exoskeletons for walking and running on slopes or for educational purposes in gait studies.
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Neutral
Effect of using real motion versus predicted motion as input for digital human modeling of back and shoulder loads during manual material handling
The study evaluates the differences in L4/L5 compression force and shoulder torques during a work process calculated using Digital Human Modeling (DHM) with motion prediction (Jack by Siemens) and DHM with experimental data. The work process is a sequential removing, carrying, and depositing task performed by nine females and nine males and recorded using a motion capture system. The analysis shows that using experimental data results in larger back compression force during the removing task, similar force during the depositing task, and less force during the carrying task.
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Enhancing motion tracking accuracy of a low-cost 3D video sensor using a biomechanical model, sensor fusion, and deep learning
The article discusses the use of low-cost 3D video sensors and recurrent neural networks (RNN) for rehabilitation purposes. The accuracy of the extracted skeleton data from these sensors can be improved using a motion tracker. However, training an RNN requires a considerable amount of relevant and accurate training data, which can be obtained using gold-standard motion tracking sensors. The article suggests a method for generating accurate skeleton data suitable for training an RNN motion tracker based on the offline fusion of a Kinect 3D video sensor and an electronic goniometer.
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Mechanics of walking and running up and downhill: A joint-level perspective to guide design of lower-limb exoskeletons
The article discusses a study on the mechanics of walking and running on different inclines and declines, and how this knowledge can inform the design of lower-limb wearable robotic devices. The study found that the distribution of limb-joint positive mechanical power shifts to the hip for incline walking and running, and the distribution of limb-joint negative mechanical power shifts to the knee for decline walking and running. The authors suggest three distinct use-modes for future lower-limb exoskeleton designs: energy injection, energy extraction, and energy transfer.
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Passive Knee Assistance Affects Whole-Body Biomechanics during Sit-to-Stand
The IEEE Engineering in Medicine and Biology Society conducted a study on the biomechanical effects of a passive elastic orthotic device designed to assist in sit-to-stand (STS) motions. The device provides bilateral knee extension assistance. Initial human experiments showed that the device resulted in a decrease in human knee torque and changes in whole-body biomechanics. The results suggest that single-joint assistance at the knee can facilitate successful STS and positively alter whole-body biomechanics.
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Preferred walking speed on rough terrain: Is it all about energetics?
A study was conducted to understand the energy expenditure of humans while walking on different terrains. It was hypothesized that the objective function for walking is more complex than only minimizing the cost of transport (COT). The study found that the preferred walking speeds of humans were not significantly different on smooth and rough terrains. However, the COT values on rough terrain were approximately 115% greater. It was concluded that the objective function for speed preference when walking on rough terrain includes COT and additional factors such as stability.
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Spinal moments during continuous sequential lifting, carrying, and lowering
The article is a copyright notice from Elsevier B.V. and its licensors and contributors. It mentions that all rights are reserved, including those for text and data mining, AI training, and similar technologies. The content on the site is maintained by Aranne Library staff at Ben-Gurion University.
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Age-related differences in pelvic and trunk motion and gait adaptability at different walking speeds
The study investigates age-related changes in gait kinematics and in kinematic adaptations over a wide range of walking velocities. The study found that older adults had lower pelvic, trunk tROM and shorter strides and stride time compared with the younger adults. As the treadmill speed was gradually increased, the older adults showed an inability to change the pelvic list angular motions between different walking velocities, while the younger adults showed changes as a function of the walking velocity. The study concluded that older adults were unable to make adaptations in pelvic and trunk kinematics between different walking speeds, while the younger adults showed more flexible behavior.
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The effect of plantar flexor muscle fatigue on postural control
The study investigates the impact of plantar flexor fatigue on postural control. Ten healthy young volunteers were tested under five conditions, including non-fatigue standing on firm surface and foam, ankle plantar flexor fatigue standing on firm surface and foam, and upper limb fatigue standing on firm surface. The results showed that plantar flexor fatigue significantly affected postural control, especially in the anteroposterior direction. The findings suggest that postural corrections occurred at a higher threshold of sway during plantar flexor fatigue compared to non-fatigue conditions.
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Improving Net Joint Torque Calculations Through a Two-Step Optimization Method for Estimating Body Segment Parameters
The article discusses a research paper by Raziel Riemer and Elizabeth T. Hsiao-Wecksler, which proposes a method for improving the accuracy of calculated net joint torques by optimizing for subject-specific body segment parameters (BSP) in the presence of characteristic and random errors in motion data measurements. The approach minimized the differences between known ground reaction forces (GRFs) and the GRF calculated via a top-down inverse dynamics approach. The researchers found that the optimized BSP reduced the error by 77%, suggesting that errors in calculated net joint torques due to traditionally-derived BSP estimates could be reduced substantially using this optimization approach.
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Improving joint torque calculations: Optimization-based inverse dynamics to reduce the effect of motion errors
The article discusses a study that presents a method to increase the accuracy of estimated joint torques through the optimization of angular position data used to describe body segment motions. The method uses a cost function that minimizes the difference between the known ground reaction forces (GRFs) and the GRF calculated via a top-down inverse dynamics solution. The study found that compared to traditional approaches, the optimization approach reduced the root mean square error (RMSE) values of joint torques derived from each approach relative to the expected true joint torques by 54% to 79%.
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MotionAnalytics Team
Employee Info
| Employees (range) | 1-10 |
| Exact count | 7 |
| Team members | 3 |
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Admin Info
| Confidence | 80/100 |
| Missing | sector, funding rounds, markets, external profiles, not claimed |
| BI Verification | Matan Eblagon |
| Last updater | Matan Eblagon |
| Updater email | matane@sncentral.org |
| Last update | 2024-07-18T00:00:00.000Z |
| Created | 2024-03-14T00:00:00.000Z |
| Claimed | Yes |