Kourosh Khoshelham

Dr. Kourosh Khoshelham

University of Melbourne
orcid

Research Focus & Metrics

Academic Career Length

23Years (2004-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2012 in Sensors
Verified
Journal Article
Open access
PDF file
KKKourosh Khoshelham
Kourosh Khoshelham
Sander Oude ElberinkSander Oude Elberink
Sander Oude Elberink
Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.

13 January 2022 in Medical & Biological Engineering & Computing
Verified
Journal Article
Closed access
PDF file
HRHatice Catal Reis
Hatice Catal Reis
Veysel TurkVeysel Turk
Veysel Turk
KKKourosh Khoshelham
Kourosh Khoshelham
SKSerhat Kaya
Serhat Kaya

22 March 2017 in IEEE Sensors Journal
Verified
Journal Article
Closed access
Fuqiang GuFuqiang Gu
Fuqiang Gu
KKKourosh Khoshelham
Kourosh Khoshelham
JSJianga Shang
Jianga Shang
FYFangwen Yu
Fangwen Yu
ZWZhuo Wei
Zhuo Wei
Robust and accurate step counting is important for indoor localization algorithms that rely on smartphone inertial sensors. Existing solutions for step counting do not consider users' false walking state (e.g., when a user in still state uses her phone for texting, playing games, and watching movies), which results in the over-counting problem. In this paper, we propose a robust and accurate step counting algorithm to solve the overcounting problem caused by false walking. Experimental results show that the proposed algorithm outperforms the commonly-used peak detection-based method and can improve the step counting accuracy by 6.56% for normal walking case, 9.54% for free walking case, and by 58.92% for false walking case, which is a significant improvement in the accuracy and robustness. We also compare the proposed method with popular commercial step counting applications including S Health, i-Health, and Pedometer++, which shows that our method can achieve better accuracy.

3 September 2012 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Verified
Journal Article
Open access
PDF file
KKKourosh Khoshelham
Kourosh Khoshelham
This paper presents an investigation of the geometric quality of depth data obtained by the Kinect sensor. Based on the mathematical model of depth measurement by the sensor a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimetres up to about 4 cm at the maximum range of the sensor. The accuracy of the data is also found to be influenced by the low resolution of the depth measurements.

4 April 2018 in IEEE Internet of Things Journal
Verified
Journal Article
Closed access
Fuqiang GuFuqiang Gu
Fuqiang Gu
KKKourosh Khoshelham
Kourosh Khoshelham
SVShahrokh Valaee
Shahrokh Valaee
JSJianga Shang
Jianga Shang
RZRui Zhang
Rui Zhang
Locomotion activity recognition (LAR) is important for a number of applications, such as indoor localization, fitness tracking, and aged care. Existing methods usually use handcrafted features, which requires expert knowledge and is laborious, and the achieved result might still be suboptimal. To relieve the burden of designing and selecting features, we propose a deep learning method for LAR by using data from multiple sensors available on most smart devices. Experimental results show that the proposed method, which learns useful features automatically, outperforms conventional classifiers that require the hand-engineering of features. We also show that the combination of sensor data from four sensors (accelerometer, gyroscope, magnetometer, and barometer) achieves a higher accuracy than other combinations or individual sensors.

10 October 2018 in IEEE Transactions on Instrumentation and Measurement
Verified
Journal Article
Closed access
Fuqiang GuFuqiang Gu
Fuqiang Gu
KKKourosh Khoshelham
Kourosh Khoshelham
CYChunyang Yu
Chunyang Yu
JSJianga Shang
Jianga Shang
Pedestrian dead reckoning (PDR) is a popular indoor localization method due to its independence of additional infrastructures and the wide availability of smart devices. Step length estimation is a key component of PDR, which has an important influence on the performance of PDR. Existing step length estimation models suffer from various limitations such as requiring knowledge of user's height, lack of consideration of varying phone carrying ways, and dependence on spatial constraints. To solve these problems, we propose a deep learning-based step length estimation model, which can adapt to different phone carrying ways and does not require individual stature information and spatial constraints. Experimental results show that the proposed method outperforms existing popular step length estimation methods.