scholarly journals CoolMoves

2021 ◽  
Vol 5 (2) ◽  
pp. 1-23
Author(s):  
Karan Ahuja ◽  
Eyal Ofek ◽  
Mar Gonzalez-Franco ◽  
Christian Holz ◽  
Andrew D. Wilson
Keyword(s):  
Human Motion ◽  
Body Motion ◽  
Weighted Distance ◽  
Prediction Probability ◽  
Technical Evaluation ◽  
Spatio Temporal ◽  
Joint Prediction ◽  
Input Cues ◽  
Motion Quality ◽  
Full Body

Current Virtual Reality (VR) systems are bereft of stylization and embellishment of the user's motion - concepts that have been well explored in animations for games and movies. We present CooIMoves, a system for expressive and accentuated full-body motion synthesis of a user's virtual avatar in real-time, from the limited input cues afforded by current consumer-grade VR systems, specifically headset and hand positions. We make use of existing motion capture databases as a template motion repository to draw from. We match similar spatio-temporal motions present in the database and then interpolate between them using a weighted distance metric. Joint prediction probability is then used to temporally smooth the synthesized motion, using human motion dynamics as a prior. This allows our system to work well even with very sparse motion databases (e.g., with only 3-5 motions per action). We validate our system with four experiments: a technical evaluation of our quantitative pose reconstruction and three additional user studies to evaluate the motion quality, embodiment and agency.

scholarly journals Incremental learning of full body motion primitives and their sequencing through human motion observation

2011 ◽  
Vol 31 (3) ◽  
pp. 330-345 ◽  
Author(s):  
Dana Kulić ◽  
Christian Ott ◽  
Dongheui Lee ◽  
Junichi Ishikawa ◽  
Yoshihiko Nakamura
Keyword(s):  
Incremental Learning ◽  
Human Motion ◽  
Body Motion ◽  
Motion Primitives ◽  
Full Body

scholarly journals An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Chengyu Guo ◽  
Jie Liu ◽  
Xiaohai Fan ◽  
Aihong Qin ◽  
Xiaohui Liang
Keyword(s):  
Low Cost ◽  
Recognition Rate ◽  
Recognition Task ◽  
Graph Model ◽  
Autoregressive Process ◽  
Human Motion ◽  
Lower Limbs ◽  
Body Motion ◽  
Fusion Model ◽  
Full Body

This paper presents a method to recognize continuous full-body human motion online by using sparse, low-cost sensors. The only input signals needed are linear accelerations without any rotation information, which are provided by four Wiimote sensors attached to the four human limbs. Based on the fused hidden Markov model (FHMM) and autoregressive process, a predictive fusion model (PFM) is put forward, which considers the different influences of the upper and lower limbs, establishes HMM for each part, and fuses them using a probabilistic fusion model. Then an autoregressive process is introduced in HMM to predict the gesture, which enables the model to deal with incomplete signal data. In order to reduce the number of alternatives in the online recognition process, a graph model is built that rejects parts of motion types based on the graph structure and previous recognition results. Finally, an online signal segmentation method based on semantics information and PFM is presented to finish the efficient recognition task. The results indicate that the method is robust with a high recognition rate of sparse and deficient signals and can be used in various interactive applications.

Acceleration Factor Modeling of Flexible Electronic Substrates From Actual Human Body Measurements

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Vikas Yadav ◽  
Jinesh Narangaparambil ◽  
Wei Liu
Keyword(s):  
Human Body ◽  
Flexible Electronics ◽  
Human Motion ◽  
Acceleration Factor ◽  
Body Motion ◽  
Motion Data ◽  
Factor Modeling ◽  
Electronic Substrates ◽  
Resistance Data ◽  
Full Body

Abstract The use of flexible electronics wearable applications has prompted the need to understand the stresses imposed during human motion for a range of activities. Wearable applications may involve situations in which the electronics may be flexed-to-install, stretched or subjected to thousands cycles of dynamic flexing. In order to develop meaningful test-levels, a better understanding is needed of the use-cases, variance, and the acceleration factors. In this study, the human body motion data for walking, jumping, squats, lunges, and bicep curls were measured using a set of ten Vicon cameras to measure the position, velocity, and accelerations of a standard full-body sensor location of the human body. In addition, reliability data has been gathered on test vehicles subjected to dynamic flexing. Continuous resistance data have been gathered on circuits subjected to dynamic flexing till failure for some of the commonly used trace geometries in electronic circuits. Experimental measurements during the accelerated tests of the boards were combined with the human body motion data to model the acceleration factor for different human activities based on the flexing angles. Human motion for multiple subjects and multiple joints has been correlated to the test levels for the development of acceleration factors. Statistical analysis on the variation of the joint angles with hypothesis testing has been conducted for different subjects and for different human body actions. Acceleration factors models have been developed for walking, jumping, squats, lunges, and bicep curls.

scholarly journals Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets

2021 ◽  
Author(s):  
Maria Lucia Parrella ◽  
Giuseppina Albano ◽  
Cira Perna ◽  
Michele La Rocca
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Sample Selection ◽  
Sample Path ◽  
Temporal Relationships ◽  
Empirical Performance ◽  
Spatio Temporal ◽  
Joint Prediction ◽  
Point Forecast ◽  
Prediction Regions

AbstractMissing data reconstruction is a critical step in the analysis and mining of spatio-temporal data. However, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. To take into account the uncertainty in the point forecast, some prediction intervals may be of interest. In particular, for (possibly long) missing sequences of consecutive time points, joint prediction regions are desirable. In this paper we propose a bootstrap resampling scheme to construct joint prediction regions that approximately contain missing paths of a time components in a spatio-temporal framework, with global probability $$1-\alpha $$ 1 - α . In many applications, considering the coverage of the whole missing sample-path might appear too restrictive. To perceive more informative inference, we also derive smaller joint prediction regions that only contain all elements of missing paths up to a small number k of them with probability $$1-\alpha $$ 1 - α . A simulation experiment is performed to validate the empirical performance of the proposed joint bootstrap prediction and to compare it with some alternative procedures based on a simple nominal coverage correction, loosely inspired by the Bonferroni approach, which are expected to work well standard scenarios.

scholarly journals Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Chunjie Chen ◽  
Xinyu Wu ◽  
Du-xin Liu ◽  
Wei Feng ◽  
Can Wang
Keyword(s):  
Gait Cycle ◽  
Human Motion ◽  
Experimental Result ◽  
Motion Patterns ◽  
Exoskeleton Robot ◽  
Gait Phase ◽  
Phase Recognition ◽  
Motion Intention ◽  
Full Body ◽  
Testing Set

The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS), which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on k-Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

Deep Full-Body Motion Network for a Soft Wearable Motion Sensing Suit

2019 ◽  
Vol 24 (1) ◽  
pp. 56-66 ◽  
Author(s):  
Dooyoung Kim ◽  
Junghan Kwon ◽  
Seunghyun Han ◽  
Yong-Lae Park ◽  
Sungho Jo
Keyword(s):  
Body Motion ◽  
Motion Sensing ◽  
Full Body

scholarly journals Co-Reading as a Generative Ontology

2018 ◽  
Vol 6 (3) ◽  
pp. 151-163
Author(s):  
Simon Biggs
Keyword(s):  
Dark Matter ◽  
Motion Tracking ◽  
Interactive Systems ◽  
Body Motion ◽  
Interactive System ◽  
Assemblage Theory ◽  
Multi Agent ◽  
Technical Considerations ◽  
Full Body

This paper discusses the immersive full body motion tracking installation Dark Matter, developed by the author and completed in early 2016. The paper outlines the conceptual focus of the project, including the use of the metaphor of dark matter to explore questions around interactive systems and assemblage. The primary technical considerations involved in the project are also outlined. ‘Co-reading' is proposed as a framework for a generative ontology, within the context of assemblage theory, deployed within a multimodal multi-agent interactive system.

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