A new PCB-based low-cost accelerometer for human motion sensing

Qiao, Dapeng; Pang, G.K.H.; Kit, Mui Man; Lam, David Chuen Chun

doi:10.1109/ical.2008.4636119

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…High-sampling-rate IMUs have been used for research purposes [4,6,7,[10][11][12], but podiatrists often work with IMUs that are affordable yet have sufficient functionality to assist in the final diagnosis; thus, they often use devices with low capture frequencies [5,8,9,13,14]. Studies have shown that a frequency of 12 Hz is sufficient to capture human body movement across different devices [15,16]. Some research even employed band-pass-filtered data with a 12 Hz cut-off frequency to analyze kinematics at varying running speeds (between 2.68 m/s and 4.47 m/s) [17].…”

Section: Introductionmentioning

confidence: 99%

Effect of Foot Orthoses on Angular Velocity of Feet

Florenciano Restoy,

Solé-Casals,

Borràs-Boix

2023

Sensors

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There is some uncertainty regarding how foot orthoses (FO) affect the biomechanics of the lower extremities during running in non-injured individuals. This study aims to describe the behavior of the angular velocity of the foot in the stride cycle measured with a low-sampling-rate IMU device commonly used by podiatrists. Specific objectives were to determine if there are differences in angular velocity between the right and left foot and to determine the effect of foot orthoses (FO) on the 3D angular velocity of the foot during running. The sample was composed of 40 male adults (age: 43.0 ± 13.8 years, weight: 72.0 ± 5.5 kg, and height: 175.5 ± 7.0 cm), who were healthy and without any locomotor system alterations at the time of the test. All subjects use FO on a regular basis. The results show that there are significant differences in the transverse plane between feet, with greater differences in the right foot. Significant differences between FO and non-FO conditions were observed in the frontal and transverse planes on the left foot and in the sagittal and transverse planes on the right foot. FO decreases the velocity of the foot in dorsi-plantar flexion and abduction and increases the velocity in inversion. The kinematic changes in foot velocity occur between 30% and 60% of the complete cycle, and the FO reduces the velocity in abduction and dorsi–plantar flexion and increases the velocity in inversion–eversion, which facilitates the transition to the oscillating leg and with it the displacement of the center of mass. Quantifying possible asymmetries and assessing the effect of foot orthoses may aid in improving running mechanics and preventing injuries in individuals.

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Section: Introductionmentioning

confidence: 99%

Effect of Foot Orthoses on Angular Velocity of Feet

Florenciano Restoy,

Solé-Casals,

Borràs-Boix

2023

Sensors

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Experimental implementation of skeleton tracking for collision avoidance in collaborative robotics

Forlini,

Neri,

Ciccarelli

et al. 2024

Int J Adv Manuf Technol

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Collaborative robotic manipulators can stop in case of a collision, according to the ISO/TS 15066 and ISO 10218-1 standards. However, in a human-robot collaboration scenario where the robot and the human share the workspace, a better solution with concerns to production and operator safety would be to perceive in advance the presence of an obstacle and be able to avoid it, thereby completing the task without halting the robot. In this paper, an obstacle avoidance algorithm is tested using a sensor system for real-time human detection; the operator represents a potential dynamic obstacle that can interfere with the robot motion. The sensor system consists of three RGB-D cameras. A custom software framework has been developed in Python exploiting machine learning tools for human skeleton detection. The coordinates of the human body joints relative to the manipulator base are used as input to the obstacle avoidance algorithm. The use of multiple sensors makes it possible to limit the occlusion problem; in addition, the choice of non-wearable sensors goes in the direction of better operator comfort. A series of experimental tests were performed to verify the accuracy of skeleton detection and the ability of the system to avoid obstacles in real time. The human motion caption system, in particular, was validated through a comparison with a commercial system based on wearable IMU sensors widely used and validated in motion capture. There is an NRMSE of $$3.23\%$$ 3.23 % for the RGB-D camera-based skeleton detection system, against an NRMSE of $$12.23\%$$ 12.23 % for the IMU wearable sensor system. Test results confirm that the system is able to avoid collisions with the human body under various conditions, static or dynamic, ensuring a minimum safety distance to any part of the manipulator. In 44 tests in which the operator moves around the robot with possible collisions (at a speed typical of manufacturing operations), the minimum operator-robot distance averaged $$208\,\textrm{mm}$$ 208 mm , being $$200\,\textrm{mm}$$ 200 mm the limit safety distance set by algorithm.

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