The goal of this project is to develop an easy-to-use data analysis system for fast, accurate, and robust estimation of the multijoint movement trajectories of the human body. Such technology has a number of medical applications, including clinical gait analysis, physical medicine and rehabilitation, sports medicine and injury prevention, quantitative assessment of motor dysfunction, design and fitting of orthoses and prostheses, feedback control of neuromuscular stimulators, calibration of implantable sensors. Access to reliable multijoint estimation tools is also a prerequisite for continued progress in basic motor control research on both humans and other species. Motion capture hardware has become widely available, and allows fast and reasonably accurate measurement of the position, orientation, bending, acceleration, etc. of various makers attached to the body. The available data analysis tools, however, lag behind these hardware advances; estimating the configuration of a multiarticulate body to which markers are non-rigidly attached remains a challenging problem. In particular, a) existing methods assume rigid marker attachment and provide no estimate of the errors resulting from unavoidable soft tissue deformation and miscalibration; b) placing markers at predetermined locations and measuring limb sizes for each subject requires prolonged setup sessions; c) the reliance on sensor-specific estimation methods makes it difficult to utilize new sensor modalities or placements; d) the redundancy in the sensor data due to the body structure is rarely exploited to handle missing data, marker misidentification, and noise in general; e) kinematic estimation is performed separate from dynamics and therefore can produce dynamically impossible trajectories; f) the few existing systems that utilize more general iterative minimization techniques do not guarantee real-time performance; g) most existing systems are tailored to the needs of the computer animation industry and do not even attempt to meet the accuracy requirements for research and clinical tools; h) investigators who need such tools are faced with the daunting task of developing their own. We propose to develop an integrated system that addresses all of the above problems. Our approach is based on a general probabilistic formulation, which allows us to apply a combination of modern statistical estimation, numerical optimization, and software engineering techniques. We believe that the multiple core methodologies needed to develop such a solution are already available, albeit in different literatures, and the time is ripe to bring them together. Our longterm goal is to provide a satisfying solution to the problem of marker-based multijoint estimation, as well as to incorporate the system proposed here into a larger suite of software tools for biomechanical analysis and simulation that is currently being developed at USC. The proposed system will not only be used in our own research, but will be documented and made available to other investigators interested in complex many-degree-of-freedom movements. ? ? ?
| Valero-Cuevas, Francisco J; Venkadesan, Madhusudhan; Todorov, Emanuel (2009) Structured variability of muscle activations supports the minimal intervention principle of motor control. J Neurophysiol 102:59-68 |
| Todorov, Emanuel (2007) Probabilistic inference of multijoint movements, skeletal parameters and marker attachments from diverse motion capture data. IEEE Trans Biomed Eng 54:1927-39 |
| Liu, Dan; Todorov, Emanuel (2007) Evidence for the flexible sensorimotor strategies predicted by optimal feedback control. J Neurosci 27:9354-68 |
| Todorov, Emanuel (2005) Stochastic optimal control and estimation methods adapted to the noise characteristics of the sensorimotor system. Neural Comput 17:1084-108 |
| Todorov, Emanuel; Li, Weiwei; Pan, Xiuchuan (2005) From task parameters to motor synergies: A hierarchical framework for approximately-optimal control of redundant manipulators. J Robot Syst 22:691-710 |
| Todorov, Emanuel (2004) Optimality principles in sensorimotor control. Nat Neurosci 7:907-15 |
