New Method for Reduced-Number IMU Estimation in Observing Human Joint Motion

Hoang, Thang; Shiao, Yaojung

doi:10.3390/s23125712

Cited by 3 publications

(2 citation statements)

References 27 publications

(35 reference statements)

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“…These devices can be inte- Accelerometers embedded in IMUs can be used in determining the movement patterns and velocity in the resistance training period [24]. Importantly, the use of dedicated algorithms is required to assess the proper execution of the exercises and evaluate the quality of the motion [57][58][59]. A common application of accelerometers is the measure of the velocity at which the resistance equipment or tool is moved.…”

Section: Force Sensors For Measuring the Amount Of Force Generatedmentioning

confidence: 99%

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

de Beukelaar,

Mantini

2023

Bioengineering

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Resistance training is an exercise modality that involves using weights or resistance to strengthen and tone muscles. It has become popular in recent years, with numerous people including it in their fitness routines to ameliorate their strength, muscle mass, and overall health. Still, resistance training can be complex, requiring careful planning and execution to avoid injury and achieve satisfactory results. Wearable technology has emerged as a promising tool for resistance training, as it allows monitoring and adjusting training programs in real time. Several wearable devices are currently available, such as smart watches, fitness trackers, and other sensors that can yield detailed physiological and biomechanical information. In resistance training research, this information can be used to assess the effectiveness of training programs and identify areas for improvement. Wearable technology has the potential to revolutionize resistance training research, providing new insights and opportunities for developing optimized training programs. This review examines the types of wearables commonly used in resistance training research, their applications in monitoring and optimizing training programs, and the potential limitations and challenges associated with their use. Finally, it discusses future research directions, including the development of advanced wearable technologies and the integration of artificial intelligence in resistance training research.

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Section: Force Sensors For Measuring the Amount Of Force Generatedmentioning

confidence: 99%

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

de Beukelaar,

Mantini

2023

Bioengineering

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“…The sensors themselves are subject to the risk of measurement errors resulting from, for example, time-variant sensor biases and measurement noise. These errors are limited by the constantly developing mathematical models, allowing precise measurement even from a reduced number of sensors [14,15].…”

Section: Introductionmentioning

confidence: 99%

Validity and Reliability of Inertial Measurement Units in Active Range of Motion Assessment in the Hip Joint

Stołowski,

Niedziela,

Lubiatowski

et al. 2023

Sensors

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Measuring joint range of motion has traditionally occurred with a universal goniometer, inclinometer, or expensive laboratory systems. The popularity of the inertial measurement unit (IMU) is growing, enabling the small and even single portable device to measure the range of motion. Until now, they were not used to assess hip joint range of motion. Our study aimed to check the validity of IMUs in assessing hip range of motion and compare them to other measurement devices—universal goniometer and inclinometer. Twenty participants completed three hip movements (flexion in standing and prone internal and external rotation) on both hips. Two testers independently assessed each movement with a goniometer, digital inclinometer, and IMU at different time points. To assess the agreement of active hip ROM between devices, Intraclass Correlation Coefficient (ICC) and Bland–Altman analysis were used. Furthermore, inter-rater and intra-rater reliability were also assessed by using ICC and Bland–Altman analysis. Limits of agreement (LOA) were calculated using Bland–Altman plots. The IMU demonstrated good to excellent validity (ICC: 0.87–0.99) compared to the goniometer and digital inclinometer, with LOAs < 9°, across all tested movements. Intra-rater reliability was excellent for all devices (ICC: 0.87–0.99) with LOAs < 7°. However, inter-rater reliability was moderate for flexion (ICC: 0.58–0.59, LOAs < 22.4) and poor for rotations (ICC: −0.33–0.04, LOAs < 7.8°). The present study shows that a single inertial measurement unit (RSQ Motion, RSQ Technologies, Poznan, Poland) could be successfully used to assess the active hip range of motion in healthy subjects, comparable to other methods accuracy.

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Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor

Shiao,

Chen,

Hoang

2024

Sensors

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Recently, posture recognition technology has advanced rapidly. Herein, we present a novel posture angle calculation system utilizing a single inertial measurement unit and a spatial geometric equation to accurately identify the three-dimensional (3D) motion angles and postures of both the upper and lower limbs of the human body. This wearable system facilitates continuous monitoring of body movements without the spatial limitations or occlusion issues associated with camera-based methods. This posture-recognition system has many benefits. Providing precise posture change information helps users assess the accuracy of their movements, prevent sports injuries, and enhance sports performance. This system employs a single inertial sensor, coupled with a filtering mechanism, to calculate the sensor’s trajectory and coordinates in 3D space. Subsequently, the spatial geometry equation devised herein accurately computed the joint angles for changing body postures. To validate its effectiveness, the joint angles estimated from the proposed system were compared with those from dual inertial sensors and image recognition technology. The joint angle discrepancies for this system were within 10° and 5° when compared with dual inertial sensors and image recognition technology, respectively. Such reliability and accuracy of the proposed angle estimation system make it a valuable reference for assessing joint angles.

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New Method for Reduced-Number IMU Estimation in Observing Human Joint Motion

Cited by 3 publications

References 27 publications

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions

Validity and Reliability of Inertial Measurement Units in Active Range of Motion Assessment in the Hip Joint

Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor

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