A Low Cost System for 3D Motion Analysis Using Microsoft Kinect

Pham, Hai Trieu; Kim, Jung Ja; Won, Yong Gwan

doi:10.4028/www.scientific.net/amm.284-287.1996

Cited by 6 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Looking forward, once the motion variables needed for classification have been identified, they could serve in a guideline for future use with less accurate measurements. Recent technological developments (e.g., Microsoft Kinect and Intel Real Sense depth cameras [ 89 , 90 ] or pose estimation tools using regular RGB cameras to detect the human skeleton in 2D and 3D [ 91 ]), albeit less accurate, may be applicable for emotion recognition in the home setting. Finally, an examination of similar effects among the elderly population also merits attention due to the potential application of such findings to the development of elder-assisting devices.…”

Section: Discussionmentioning

confidence: 99%

Emotion and motion: Toward emotion recognition based on standing and walking

Riemer,

Joseph,

Lee

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Emotion recognition is key to interpersonal communication and to human–machine interaction. Body expression may contribute to emotion recognition, but most past studies focused on a few motions, limiting accurate recognition. Moreover, emotions in most previous research were acted out, resulting in non–natural motion, which is unapplicable in reality. We present an approach for emotion recognition based on body motion in naturalistic settings, examining authentic emotions, natural movement, and a broad collection of motion parameters. A lab experiment using 24 participants manipulated participants’ emotions using pretested movies into five conditions: happiness, relaxation, fear, sadness, and emotionally–neutral. Emotion was manipulated within subjects, with fillers in between and a counterbalanced order. A motion capture system measured posture and motion during standing and walking; a force plate measured center of pressure location. Traditional statistics revealed nonsignificant effects of emotions on most motion parameters; only 7 of 229 parameters demonstrate significant effects. Most significant effects are in parameters representing postural control during standing, which is consistent with past studies. Yet, the few significant effects suggest that it is impossible to recognize emotions based on a single motion parameter. We therefore developed machine learning models to classify emotions using a collection of parameters, and examined six models: k-nearest neighbors, decision tree, logistic regression, and the support vector machine with radial base function and linear and polynomial functions. The decision tree using 25 parameters provided the highest average accuracy (45.8%), more than twice the random guess for five conditions, which advances past studies demonstrating comparable accuracies, due to our naturalistic setting. This research suggests that machine learning models are valuable for emotion recognition in reality and lays the foundation for further progress in emotion recognition models, informing the development of recognition devices (e.g., depth camera), to be used in home-setting human–machine interactions.

show abstract

Section: Discussionmentioning

confidence: 99%