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A Markerless 3D Computerized Motion Capture System Incorporating a Skeleton Model for Monkeys.

ABSTRACT: In this study, we propose a novel markerless motion capture system (MCS) for monkeys, in which 3D surface images of monkeys were reconstructed by integrating data from four depth cameras, and a skeleton model of the monkey was fitted onto 3D images of monkeys in each frame of the video. To validate the MCS, first, estimated 3D positions of body parts were compared between the 3D MCS-assisted estimation and manual estimation based on visual inspection when a monkey performed a shuttling behavior in which it had to avoid obstacles in various positions. The mean estimation error of the positions of body parts (3-14 cm) and of head rotation (35-43°) between the 3D MCS-assisted and manual estimation were comparable to the errors between two different experimenters performing manual estimation. Furthermore, the MCS could identify specific monkey actions, and there was no false positive nor false negative detection of actions compared with those in manual estimation. Second, to check the reproducibility of MCS-assisted estimation, the same analyses of the above experiments were repeated by a different user. The estimation errors of positions of most body parts between the two experimenters were significantly smaller in the MCS-assisted estimation than in the manual estimation. Third, effects of methamphetamine (MAP) administration on the spontaneous behaviors of four monkeys were analyzed using the MCS. MAP significantly increased head movements, tended to decrease locomotion speed, and had no significant effect on total path length. The results were comparable to previous human clinical data. Furthermore, estimated data following MAP injection (total path length, walking speed, and speed of head rotation) correlated significantly between the two experimenters in the MCS-assisted estimation (r = 0.863 to 0.999). The results suggest that the presented MCS in monkeys is useful in investigating neural mechanisms underlying various psychiatric disorders and developing pharmacological interventions.

SUBMITTER: Nakamura T

PROVIDER: S-EPMC5094601 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:Investigations into the possible associations between early in life motor function and later in life musculoskeletal health, will require easily obtainable, valid, and reliable measures of gross motor function and kinematics. Marker-based motion capture systems provide reasonably valid and reliable measures, but recordings are restricted to expensive lab environments. Markerless motion capture systems can provide measures of gross motor function and kinematics outside of lab environments and with minimal interference to the subjects being investigated. It is, however, unknown if these measures are sufficiently valid and reliable in young children to warrant further use. This study aims to document the concurrent validity of a markerless motion capture system: "The Captury." Method:Measures of gross motor function and lower extremity kinematics from 14 preschool children (age between three and 6 years) performing a series of squats and standing broad jumps were recorded by a marker-based (Vicon) and a markerless (The Captury) motion capture system simultaneously, in December 2015. Measurement differences between the two systems were examined for the following variables: jump length, jump height, hip flexion, knee flexion, ankle dorsi flexion, knee varus, knee to hip separation distance ratio (KHR), ankle to hip separation distance ratio (AHR), frontal plane projection angle, frontal plane knee angle (FPKA), and frontal plane knee deviation (FPKD). Measurement differences between the systems were expressed in terms of root mean square errors, mean differences, limits of agreement (LOA), and intraclass correlations of absolute agreement (ICC (2,1) A) and consistency of agreement. Results:Measurement differences between the two systems varied depending on the variables. Agreement and reliability ranged from acceptable for e.g. jump height [LOA: -?3.8?cm to 2.2?cm; ICC (2,1) A: 0.91] to unacceptable for knee varus [LOA: -?33° to 19°; ICC (2,1) A: 0.29]. Conclusions:The measurements by the markerless motion capture system "The Captury" cannot be considered interchangeable with the Vicon measures, but our results suggest that this system can produce estimates of jump length, jump height, KHR, AHR, knee flexion, FPKA, and FPKD, with acceptable levels of agreement and reliability. These variables are promising for use in future research but require further investigation of their clinimetric properties.

Project description:Motion capture allows accurate recording of human motion, with applications in many fields, including entertainment, medicine, sports science and human computer interaction. A common difficulty with this technology is the occurrence of missing data, due to occlusions, or recording conditions. Various models have been proposed to estimate missing data. Some are based on interpolation, low-rank properties or inter-correlations. Others involve dataset matching or skeleton constraints. While the latter have the advantage of promoting a realistic motion estimation, they require prior knowledge of skeleton constraints, or the availability of a prerecorded dataset. In this article, we propose a probabilistic averaging method of several recovery models (referred to as Probabilistic Model Averaging (PMA) in this paper), based on the likelihoods of the distances between body points. This method has the advantage of being automatic, while allowing an efficient gap data recovery. To support and validate the proposed method, we use a set of four individual recovery models, based on linear/nonlinear regression in local coordinate systems. Finally, we propose two heuristic algorithms to enforce skeleton constraints in the reconstructed motion, which can be used on any individual recovery model. For validation purposes, random gaps were introduced into motion-capture sequences, and the effects of factors such as the number of simultaneous gaps, gap length and sequence duration were analyzed. Results show that the proposed probabilistic averaging method yields better recovery than (i) each of the four individual models and (ii) two recent state-of-the-art models, regardless of gap length, sequence duration and number of simultaneous gaps. Moreover, both of our heuristic skeleton-constraint algorithms significantly improve the recovery for 7 out of 8 tested motion-capture sequences (p < 0.05), for 10 simultaneous gaps of 5 seconds. The code is available for free download at: https://github.com/numediart/MocapRecovery.

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A Markerless 3D Computerized Motion Capture System Incorporating a Skeleton Model for Monkeys.

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