Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud.

Estimation of muscle activity is very important as it can be a cue to assess a person's movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and help for various study fields. In the present paper, we propose a method to predict human muscle activity from skin surface strain. This requires us to obtain a 3D reconstruction model with a high relative accuracy. The problem is that reconstruction errors due to noise on raw data generated in a visual measurement system are inevitable. In particular, the independent noise between each frame on the time series makes it difficult to accurately track the motion. In order to obtain more precise information about the human skin surface, we propose a method that introduces a temporal constraint in the non-rigid registration process. We can achieve more accurate tracking of shape and motion by constraining the point cloud motion over the time series. Using surface strain as input, we build a multilayer perceptron artificial neural network for inferring muscle activity. In the present paper, we investigate simple lower limb movements to train the network. As a result, we successfully achieve the estimation of muscle activity via surface strain.

DATSURYOKU Sensor-A Capacitive-Sensor-Based Belt for Predicting Muscle Tension: Preliminary Results.

Excessive muscle tension is implicitly caused by inactivity or tension in daily activities, and it results in increased joint stiffness and vibration, and thus, poor performance, failure, and injury in sports. Therefore, the routine measurement of muscle tension is important. However, a co-contraction observed in excessive muscle tension cannot be easily detected because it does not appear in motion owing to the counteracting muscle tension, and it cannot be measured by conventional motion capture systems. Therefore, we focused on the physiological characteristics of muscle, that is, the increase in muscle belly cross-sectional area during activity and softening during relaxation. Furthermore, we measured muscle tension, especially co-contraction and relaxation, using a DATSURYOKU sensor, which measures the circumference of the applied part. The experiments showed high interclass correlation between muscle activities and circumference across maximal voluntary co-contractions of the thigh muscles and squats. Moreover, the circumference sensor can measure passive muscle deformation that does not appear in muscle activities. Therefore, the DATSURYOKU sensor showed the potential to routinely measure muscle tension and relaxation, thus avoiding the risk of failure and injury owing to excessive muscle tension and can contribute to the realization of preemptive medicine by measuring daily changes.

Risk estimation for intervertebral disc pressure through musculoskeletal joint reaction force simulation.

This research proposes a novel method that evaluates joint reaction forces by motion analysis using a musculoskeletal model. While general muscle tension estimations minimize the sum of the muscle tensions, the proposed method utilizes the joint reaction forces themselves in the objective function of the optimization problem in addition to conventional method. This method can estimate a pattern of the muscle tensions that maximizes or minimizes a specific joint force. As a typical outcome, the proposed method allows evaluating intervertebral disc compressive force caused by co-contraction of muscles while avoiding risk underestimation. We analyzed the actual lifting motion as an example and confirmed that the method can estimate the muscle tension distribution under different tension conditions.

Simulation-based design for robotic care device: Optimizing trajectory of transfer support robot.

This paper presents a framework of simulation-based design for robotic care devices developed to reduce the burden of caregiver and care receivers. First, physical interaction between the user and device is quantitatively estimated by using a digital human simulator. Then we introduce a method for optimizing the design parameters according to given evaluation criteria. An example of trajectory optimization of transfer support robot is provided to demonstrate the effectiveness of the proposed method.

Modeling and Identification of a Realistic Spiking Neural Network and Musculoskeletal Model of the Human Arm, and an Application to the Stretch Reflex.

This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular junctions. Feedback information from muscle spindles were relayed via monosynaptic excitatory and disynaptic inhibitory connections, to simulate spinal afferent pathways. Subject-specific model parameters were identified from human experiments by using inverse dynamics computations and optimization methods. The identified neuromuscular model was used to simulate the biceps stretch reflex and the results were compared to an independent dataset. The proposed model was able to track the recorded data and produce dynamically consistent neural spiking patterns, muscle forces and movement kinematics under varying conditions of external forces and co-contraction levels. This additional layer of detail in neuromuscular models has important relevance to the research communities of rehabilitation and clinical movement analysis by providing a mathematical approach to studying neuromuscular pathology.

In-vitro identification of shoulder joint and muscle dynamics based on motion capture and musculoskeletal computation.

Dynamics properties of shoulder joint and muscle are experimentally identified under different musculoskeletal conditions for a digital human model with accurate dynamics. Passive swing motions of scapula and upper limb bones in cadaveric specimen with and without muscles are measured by an optical motion capture system. External forces that are applied to the scapula bone are simultaneously measured by a force plate. The dynamics identification process consists of 3 steps: 1) identify the inertial parameters of the cadaveric specimen with and without muscles respectively, 2) identify the viscosity of the glenohumeral joint from the specimen without muscles, and 3) identify the viscosity of the shoulder muscles from the specimen with muscles and the identified joint viscosity. These parameters are identified in six cadaveric specimens. Their joint viscosities are 5.33E-02 ± 1.33E-02 Nms/rad (without muscles) and 1.07E-01 ± 2.28E-02 Nms/rad (with muscle), and their muscle viscosities are 6.69E+02 ± 8.11E+02 Ns/m (mean ± SD). The identified joint viscosity corresponds with the literature value. This measurement and identification algorithm would improve the dynamics of the digital human model and realize the accurate muscle activity estimation and the motion simulation.

Forward dynamics simulation of human figures on assistive devices using geometric skin deformation model.

We present a forward dynamics (FD) simulation technique for human figures when they are supported by assistive devices. By incorporating a geometric skin deformation model, called linear blend skinning (skinning), into rigid-body skeleton dynamics, we can model a time-varying geometry of body surface plausibly and efficiently. Based on the skinning model, we also derive a Jacobian (a linear mapping) that maps contact forces exerted on the skin to joint torques, which is the main technical contribution of this paper. This algorithm allows us to efficiently simulate dynamics of human body that interacts with assistive devices. Experimental results showed that the proposed approach can generate plausible motions and can estimate pressure distribution that is roughly comparable to the tactile sensor data.

Monitoring the segment parameters during long term physical training from motion capture data.

The segment parameters (SP) consisting of inertia and position of the center of mass of each segment, of the human body are crucial data when one wants investigate motion dynamics. The segment parameters vary with time according to immobilization, physical training, rehabilitation, muscular diseases. This knowledge provides valuable information to support medical diagnosis and to quantify the effect of medical treatment, rehabilitation or training. However they are usually difficult to measure in-vivo for these kinds of applications and thus are not specifically used. In this paper we propose to apply a previously developed identification method in order to monitor the evolutions of those parameters over 5 months, during which the candidate followed a 16-week marathon training before running the 2009 Tokyo Marathon. The motion data is recorded on a weekly basis and the parameters are computed after each session. The obtained results are presented and the changes in body SP are discussed in the light of typical results occurring to the body fitness.

Real-time identification and visualization of human segment parameters.

Mass parameters of the body segments are mandatory to study motion dynamics. No systematic method to estimate them has been proposed so far. Rather, parameters are scaled from generic tables or estimated with methods inappropriate for in-patient care. Based on our previous works, we propose a real-time software that allows to estimate the whole-body segment parameters, and to visualize the progresses of the completion of the identification. The visualization is used as a feedback to optimize the excitation and thus the identification results. The method is experimentally tested.

Motion capture based identification of the human body inertial parameters.

Identification of body inertia, masses and center of mass is an important data to simulate, monitor and understand dynamics of motion, to personalize rehabilitation programs. This paper proposes an original method to identify the inertial parameters of the human body, making use of motion capture data and contact forces measurements. It allows in-vivo painless estimation and monitoring of the inertial parameters. The method is described and then obtained experimental results are presented and discussed.