Human movement trajectory recording for home alone by thermopile array sensor

Multi Human Movement Trajectory Extraction by Thermal Sensor

Advanced Intelligent Systems - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-05500-8_5 ◽

2014 ◽

pp. 35-49

Author(s):

Masato Kuki ◽

Hiroshi Nakajima ◽

Naoki Tsuchiya ◽

Junichi Tanaka ◽

Yutaka Hata

Keyword(s):

Human Movement ◽

Thermal Sensor ◽

Movement Trajectory

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Human care system for heart-rate and human-movement trajectory in home and its application to detect mental disease

10.1117/12.918552 ◽

2012 ◽

Author(s):

Yutaka Hata ◽

Seigo Kanazawa ◽

Maki Endo ◽

Naoki Tsuchiya ◽

Hiroshi Nakajima

Keyword(s):

Heart Rate ◽

Mental Disease ◽

Human Movement ◽

Movement Trajectory ◽

Human Care ◽

Care System

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Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations

Entropy ◽

10.3390/e20100724 ◽

2018 ◽

Vol 20 (10) ◽

pp. 724 ◽

Cited By ~ 3

Author(s):

Dmytro Velychko ◽

Benjamin Knopp ◽

Dominik Endres

Keyword(s):

Gaussian Process ◽

Body Movement ◽

Human Movement ◽

Dynamical Model ◽

Variational Approximation ◽

Movement Trajectory ◽

Training Time ◽

Variational Approximations ◽

Natural Movement ◽

Gaussian Process Dynamical Model

We describe a sparse, variational posterior approximation to the Coupled Gaussian Process Dynamical Model (CGPDM), which is a latent space coupled dynamical model in discrete time. The purpose of the approximation is threefold: first, to reduce training time of the model; second, to enable modular re-use of learned dynamics; and, third, to store these learned dynamics compactly. Our target applications here are human movement primitive (MP) models, where an MP is a reusable spatiotemporal component, or “module” of a human full-body movement. Besides re-usability of learned MPs, compactness is crucial, to allow for the storage of a large library of movements. We first derive the variational approximation, illustrate it on toy data, test its predictions against a range of other MP models and finally compare movements produced by the model against human perceptual expectations. We show that the variational CGPDM outperforms several other MP models on movement trajectory prediction. Furthermore, human observers find its movements nearly indistinguishable from replays of natural movement recordings for a very compact parameterization of the approximation.

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Models of human movement: Trajectory planning and inverse kinematics studies

Robotics and Autonomous Systems ◽

10.1016/j.robot.2012.09.020 ◽

2013 ◽

Vol 61 (4) ◽

pp. 330-339 ◽

Cited By ~ 34

Author(s):

Tamar Flash ◽

Yaron Meirovitch ◽

Avi Barliya

Keyword(s):

Trajectory Planning ◽

Inverse Kinematics ◽

Human Movement ◽

Movement Trajectory

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Single Feedback Model of Human Goal-Directed Movement

ASME 2008 Dynamic Systems and Control Conference, Parts A and B ◽

10.1115/dscc2008-2207 ◽

2008 ◽

Author(s):

Oh-Sang Kwon ◽

Jeffrey N. Shelton ◽

George T.-C. Chiu

Keyword(s):

Early Stage ◽

Human Movement ◽

Minimum Variance ◽

Human Motion ◽

Movement Trajectory ◽

Directed Movement ◽

Psychophysical Data ◽

Proposed Model ◽

Aimed Movement ◽

Speed Accuracy

Two prominent models frequently used to explain targeted human movement are the stochastic optimized-submovement model and the minimum variance model. Both successfully explain the speed-accuracy tradeoff known as Fitts’ law, but neither is complete. The former cannot predict movement trajectory between the endpoints, while the latter is not congruent with the multiple movement segments often observed in human motion. In this paper, a new model is proposed in which an aimed movement consists of two submovements and a single feedback instant, with the trajectory of each submovement being individually optimized. Simulations using the proposed model show that the optimal transition between two submovements occurs at an early stage of the movement, and produces a sharp peak in the acceleration profile. This result is consistent with psychophysical data. Also observed in numerical simulation is the bell-shaped positional variance curve that is in agreement with psychophysical data.

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