Motion classification approach based on biomechanical analysis of human activities

Ay, Betül; Karaköse, Mehmet

doi:10.1109/iccic.2013.6724198

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Thus, the difficulty comes from the combinatorial explosion of the number of trials with the different activities and the different sensor positions considered. The Firat University [10] proposes a method of co-recognition of the human activity and the positioning of an inertial unit because they are more advantageous than cameras. The strength of the approach is to estimate both the sensor location on the human body and the motion recognition.…”

Section: Introductionmentioning

confidence: 99%

Synthetized inertial measurement units (IMUs) to evaluate the placement of wearable sensors on human body for motion recognition

Hoareau

Jodin

Chantal

et al. 2022

The Journal of Engineering

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Movement data from athletes are useful to quantify performance or more specifically the workload. Inertial measurement units (IMUs) are useful sensors to quantify body movements. Sensor placement on human body is still an open question that this paper focuses on. A method that develops synthesized inertial data is proposed for determining optimal sensors placement. Comparison between virtual and real inertial data is achieved. Training motion recognition algorithm on synthesized and real inertial data exhibits less than 7% difference. This method highlights the ability of the numerical model to determine relevant sensor placement of IMUs on human body for motion recognition algorithm using virtual sensors.

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Section: Introductionmentioning

confidence: 99%

Synthetized inertial measurement units (IMUs) to evaluate the placement of wearable sensors on human body for motion recognition

Hoareau

Jodin

Chantal

et al. 2022

The Journal of Engineering

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“…Bu da insan hareketlerini izleme, tanıma, öğrenme ve gerçekleştirme gibi yeteneklerin robotlara kazandırılması ile mümkündür. Özellikle YSA [1], Bulanık Mantık [2], Destek Vektör Makinaları (DVM) [3,4] ve AÖM gibi teknikler, insan hareketlerinin tanınması ve izlemesinde sıkça tercih edilen yöntemlerdendir [5][6][7][8][9].…”

Section: Introductionunclassified

Teaching human gestures to humanoid robots by using Kinect sensor

Yavşan

Uçar

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe-Bu çalışmada, insan hareketlerinin tanınması ve insansı bir robot üzerinde gerçekleştirilmesine yönelik yeni bir algoritma geliştirilmiştir. İki kısımdan oluşan çalışmanın birinci kısmında, eş zamanlı bir taklit sistemi sunulmaktadır. Xbox 360 Kinect algılayıcıdan elde edilen üç boyutlu iskelet eklem pozisyonları, bir dönüşüm algoritması sayesinde robotun kol eklem açılarına dönüştürülmekte ve sonrasında bu açılar, NAO insansı robota aktarılmaktadır. Bu kısmın sonunda, insan üst vücut hareketleri, NAO robot tarafından eş zamanlı olarak başarılı bir şekilde taklit edilmiştir. Çalışmanın ikinci kısmında, insan hareketlerinin tanınması için bir algoritma oluşturulmuştur. İnsan hareketlerini sınıflandırmak için Aşırı Öğrenme Makinaları (AÖM) ve geriye yayılım algoritması ile eğitilen ileri beslemeli Yapay Sinir Ağları (YSA) kullanılmıştır. Karşılaştırmalı sonuçlara göre, AÖM ile daha iyi bir tanıma başarımı elde edilmiştir. Anahtar Kelimeler -İnsan hareketlerinin tanınması; NAO insansı robot; Xbox 360 Kinect.Abstract-In this study, a novel algorithm is developed to recognize human actions and reproduce human actions on a humanoid robot. The study consists of two parts. In the first part, the real time human imitation system is realized. The three dimensional skeleton joint positions obtained from Xbox 360 Kinect. These positions are transformed to joint angles of robot arms via a transformation algorithm and these angles are transferred to NAO robot. The human upper body movements are finally successfully imitated by NAO robot in real time. In the second part, the algorithm is generated for the recognition of human actions. Extreme Learning Machines (ELMs) and the Feed Forward Neural Networks (FNNs) with back propagation algorithm are used to classify actions. According to the comparative results, ELMs produce a better recognition performance.

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Motion classification approach based on biomechanical analysis of human activities

Cited by 2 publications

References 17 publications

Synthetized inertial measurement units (IMUs) to evaluate the placement of wearable sensors on human body for motion recognition

Synthetized inertial measurement units (IMUs) to evaluate the placement of wearable sensors on human body for motion recognition

Teaching human gestures to humanoid robots by using Kinect sensor

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