Evaluation of the Foot Center of Pressure Estimation from Pressure Insoles during Sidestep Cuts, Runs and Walks

Morin, Pauline; Muller, Antoine; Pontonnier, Charles; Dumont, Georges

doi:10.3390/s22155628

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…The results of our study showed higher accuracy with lower error values in COPx (1.29 ± 0.47 mm in slower gait, 1.34 ± 0.47 mm in faster gait) compared to COPy (12.55 ± 5.08 mm in slower gait, 11.26 ± 3.89 mm in faster gait). Similar findings were reported in the studies by Zhang et al [ 22 ] and Morin et al [ 29 ]. According to their study results, locating the sensors at the top and bottom edges of the insole may be a method to minimize the error rate in COPy.…”

Section: Discussionsupporting

confidence: 92%

Customized Textile Capacitive Insole Sensor for Center of Pressure Analysis

Kim

Min

2022

Sensors

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Center of pressure refers to the centroid of the ground reaction force vector detected underneath the walking foot, which is a summary measure representing body segment movements during human locomotion. In this study, we developed a cost-effective, lightweight insole-type textile capacitive sensor (I-TCPs) to analyze plantar pressure (PP) distribution and center of pressure (COP) trajectory. To test the accuracy of I-TCPs, the measured pressure data was compared with that of F-scan. The sensor performance test was divided into a static baseline test and a dynamic gait experiment, both at two different gait speeds self-selected by the subjects. Static gait results showed that I-TCPs were capable of recognizing PP segments at different gait speeds. Dynamic gait results showed an average RMSE of 1.29 ± 0.47 mm in COPx (mediolateral shift) and 12.55 ± 5.08 mm in COPy (anteroposterior shift) at a comfortable gait speed. The COP correlation between I-TCPs and F-scan was 0.54 ± 0.09 in COPx and 0.92 ± 0.04 in COPy in comfortable gait speed conditions, in which COPy values presented a stronger correlation. RMSE and correlation in fast gait speed conditions also showed similar results. The findings of this study can be the basis for future research, including rehabilitation engineering, developing ICT devices, and creating smart wearable sensors to improve quality of life for patients and healthy individuals.

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Section: Discussionsupporting

confidence: 92%

Customized Textile Capacitive Insole Sensor for Center of Pressure Analysis

Kim

Min

2022

Sensors

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“…This paper presented a novel Bi-LSTM structure that learns bidirectional temporal dependencies in sequential data (namely, the signals acquired from heterogeneous insole-embedded sensors) to accurately estimate underfoot COP trajectories. The proposed method can achieve comparable accuracy as research-grade commercial insole systems that rely on expensive, high-density pressure-sensitive arrays [11], [59], [60]. Furthermore, compared to physics-informed approaches such as [26], the proposed method does not require timeconsuming equilibration procedures (which must be with specialized equipment) improves the and applicability of the technology, while retaining interpretability.…”

Section: Discussionmentioning

confidence: 97%

Accurate COP Trajectory Estimation in Healthy and Pathological Gait Using Multimodal Instrumented Insoles and Deep Learning Models

Duong,

Uher,

Young

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Measuring center-of-pressure (COP) trajectories in out-of-the-lab environments may provide valuable information about changes in gait and balance function related to natural disease progression or treatment in neurological disorders. Traditional equipment to acquire COP trajectories includes stationary force plates, instrumented treadmills, electronic walkways, and insoles featuring high-density force sensing arrays, all of which are expensive and not widely accessible. This study introduces novel deep recurrent neural networks that can accurately estimate dynamic COP trajectories by fusing data from affordable and heterogeneous insole-embedded sensors (namely, an eight-cell array of force sensitive resistors (FSRs) and an inertial measurement unit (IMU)). The method was validated against gold-standard equipment during out-ofthe-lab ambulatory tasks that simulated real-world walking. Root-mean-square errors (RMSE) in the mediolateral (ML) and anteroposterior (AP) directions obtained from healthy individuals (ML: 0.51 cm, AP: 1.44 cm) and individuals with neuromuscular conditions (ML: 0.59 cm, AP: 1.53 cm) indicated technical validity. In individuals with neuromuscular conditions, COP-derived metrics showed significant correlations with validated clinical measures of ambulatory function and lowerextremity muscle strength, providing proof-of-concept evidence of the convergent validity of the proposed method for clinical applications.

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“…The pressure sensors can be used to track foot pressure changes over a period, and this will help in evaluating the progress achieved. The information can be used to make changes to the resistance training program so that there can be better performance in the long term [ 66 ].…”

Section: Wearable Devices For Measuring Biomechanical Parameters Duri...mentioning

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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Evaluation of the Foot Center of Pressure Estimation from Pressure Insoles during Sidestep Cuts, Runs and Walks

Cited by 5 publications

References 26 publications

Customized Textile Capacitive Insole Sensor for Center of Pressure Analysis

Customized Textile Capacitive Insole Sensor for Center of Pressure Analysis

Accurate COP Trajectory Estimation in Healthy and Pathological Gait Using Multimodal Instrumented Insoles and Deep Learning Models

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

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