Hisham Madcor - Academia.edu (original) (raw)
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Papers by Hisham Madcor
2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), 2021
Gait refers to the displacement of the center of gravity during motion. Gait analysis has been a ... more Gait refers to the displacement of the center of gravity during motion. Gait analysis has been a focus of much research in recent years. However, the literature on gait analysis either deals with visual gait data or with inertial gait data collected using wearable sensors. In this work, we propose a new dataset collected on our campus of 45 subjects (32 males and 13 females) of ages from 18 to 23 walking a straight path while wearing 4 inertial measurement units and being filmed using two smartphones fixed at two different directions. Both the visual and inertial data are recorded at the same time, and an asynchronous signal was performed by every subject to be able to align both modalities together. We validate our data on two classification models for gender and person recognition and show that the two models perform well on our data.
Human Activity Recognition has gained tremendous drive in recent years. This is due to the increa... more Human Activity Recognition has gained tremendous drive in recent years. This is due to the increasing ubiquity of all types of sensors in commodity devices such as smartphones, smart watches, tablets, etc. This has made available to the normal user a continuous stream of data including visual data, inertial motion data, audio, etc. In this paper we focus on data streamed from inertial motion units (IMUs). Such units are currently embedded on almost all wearable devices including smart watches, wrist bands, etc. In many research works, as well as in many real applications, different specialized IMU units are mounted on different body parts. In the current work, we try to answer the following question: given the streamed inertial signals of a gait pattern, as well as some other activities, determine which sensor location on the subject’s body generated this signal. We validate our work on several datasets that contain multi-dimensional measurements from a multitude of sensors mounted ...
2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), 2021
Gait refers to the displacement of the center of gravity during motion. Gait analysis has been a ... more Gait refers to the displacement of the center of gravity during motion. Gait analysis has been a focus of much research in recent years. However, the literature on gait analysis either deals with visual gait data or with inertial gait data collected using wearable sensors. In this work, we propose a new dataset collected on our campus of 45 subjects (32 males and 13 females) of ages from 18 to 23 walking a straight path while wearing 4 inertial measurement units and being filmed using two smartphones fixed at two different directions. Both the visual and inertial data are recorded at the same time, and an asynchronous signal was performed by every subject to be able to align both modalities together. We validate our data on two classification models for gender and person recognition and show that the two models perform well on our data.
Human Activity Recognition has gained tremendous drive in recent years. This is due to the increa... more Human Activity Recognition has gained tremendous drive in recent years. This is due to the increasing ubiquity of all types of sensors in commodity devices such as smartphones, smart watches, tablets, etc. This has made available to the normal user a continuous stream of data including visual data, inertial motion data, audio, etc. In this paper we focus on data streamed from inertial motion units (IMUs). Such units are currently embedded on almost all wearable devices including smart watches, wrist bands, etc. In many research works, as well as in many real applications, different specialized IMU units are mounted on different body parts. In the current work, we try to answer the following question: given the streamed inertial signals of a gait pattern, as well as some other activities, determine which sensor location on the subject’s body generated this signal. We validate our work on several datasets that contain multi-dimensional measurements from a multitude of sensors mounted ...