Human Motion Analysis
Human Motion Analysis
Research Area

Modeling, analysis, and prediction of human motion is an important and indispensable area of research in human centered design and virtual prototyping in the automotive industry.  The application of human simulation technology toward prediction of comfort, ergonomics, occupant safety, occupant package design, human-machine interaction, and other disciplines promises to overcome limitations imposed by experimentation with real human subjects or their mechanical surrogates. ​  At HRI, we utilize research in the areas of robotics, biomechanics, computer vision, and motor control to develop technology that generates and analyzes human movement for various automotive applications, including ergonomics assessment in manufacturing, vehicle occupant package design, and physical human machine interaction.​     

To generate human movements, we adopt a differential kinematics-based approach from robotics to implement an algorithm for predicting the posture and movement of humans performing the required tasks. These algorithms generally require computing a low dimensional description of motion from a set of observations using computer vision techniques, or from wearable sensor networks such as inertial measurement units (IMUs). Earlier versions of these algorithms were developed for Honda's humanoid robotics applications. Those algorithms have been adapted for human kinematic and dynamic structures and realized in OpenSim, a widely used open-source, user extensible software system that lets users develop models of musculoskeletal structures and create kinematic or dynamic simulations of movement. The generated motion is used to drive a biomechanical simulation in OpenSim to predict various kinematic, dynamic, and energetic indicators of motion.​

Projects

We compute various musculoskeletal indicators of human performance when the driver is operating a vehicle under normal and emergency maneuvering.
The goal of the project is to achieve robust lane-level localization for cars using low cost mass producible sensors.
We have developed online algorithms to transfer motion from a human demonstrator to Honda's humanoid robot, ASIMO.
The development of the next generation green and safe batteries with high energy density is highly desirable for meeting the rapidly growing needs of electrical vehicles.
Rare earth Manganese Oxide (ReMO) as a cathode material has potential capacity higher than that of commercial lithium manganese oxide.
Metal-air batteries are solid state batteries using metal oxidation at the anode and oxygen reduction at the cathode to induce a current flow.
This project is focused on CNT and graphene enforced electrode materials for secondary battery and supercapacitor applications.
This research is focused on scale-up technology for continuous synthesis of SWNTs by CVD method and the exploration of their performance in actual electrochemical devices
The core of this project is for atomistic level understanding of environmental impact on conductivities of SWNTs and 2-D materials in order to reveal their ultimate sensitivities.
Synthesis and studies of growth mechanism, self assembly and properties of low dimensional nanomaterials for alternative energy technologies are at the core of our research.
The goal is to develop driving aids that enhance the driver's situational awareness and give drivers a sense of confidence and trust in the vehicles they are operating.
In Knowledge Discovery, we Integrate knowledge from multiple sources such as Wikipedia, Yahoo Question/Answers, Open Directory Project and OpenMind.
Probabilistic model to track dialog state and provide information to driver viaspoken dialog
This project presents a control theoretic approach for human pose estimation from a set of key feature points detected using depth image streams obtained from a time of flight imaging device.
We are developing a real-time system that detects and tracks traffic participants.
We aim to develop a robust pedestrian detection algorithm that can handle partial occlussions.
We are taking advantage of our autonomous driving platform to create a comprehensive repository of annotated sensor data that provide computer vision benchmarks and training data to support advanced driving assist and autonomous driving applications.
Backing-up of articulated vehicles poses a difficult challenge even for experienced drivers. While long wheelbase dual-axle trailers provide a benefit of increased capacity over their single-axle counterparts, backing-up of such systems is especially difficult. We devise a control strategy for such systems, allowing backing-up maneuvers to be intuitive to drivers without experience with trailers. Using hitch angle feedback, we show these concepts can be used to stabilize the trailer in back-up motion in the presence of arbitrary driver inputs.
Utilizing the latest wearable sensing technologies and patented motion prediction algorithms, the goal is to predict human movement and perform biomechanical computations based on those predictions.