A Review on Methods and Devices for Force Platforms Calibration in Medical Applications

Scorza, Andrea; Massaroni, C.; Orsini, Francesco; D’Anna, Carmen; Conforto, Silvia; Silvestri, S.; Sciuto, Salvatore Andrea

doi:10.25103/jestr.111.02

Cited by 11 publications

(7 citation statements)

References 49 publications

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“…Instrumented walkways including force platforms for analyzing overground walking exist for medical applications [64]. The COP trajectory is computed by aggregating the signals of strain gauges that are placed on the edges of the walkway every 1 to 2 m. COP trajectory assessment requires the measure of the vertical force only (i.e., a 3-component force platform [65]), which is a major simplification as compared to methods relying on 3D GRF signals that require a 6-component force platform [65]. The major issue is that the correct COP position can be obtained only when one individual at a time steps onto the sensitive area.…”

Section: Discussionmentioning

confidence: 99%

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Terrier

2020

Applied Sciences

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Fig. 6. CAM analysis. Twenty-four segments were randomly selected from the training set containing the gait data of six participants (left columns). These segments were used to fine-tune the last layers of the convolutional neural network (CNN) that was pre-trained on the training set of the 30 other participants. This CNN classified 30 segments drawn randomly from the test set with 100% accuracy (right columns). Class activation mapping (CAM) was performed on each sample of the test set. Color coding shows which parts of the signals are prioritized to be used by the CNN to perform classification. Warm color (red, orange): high focus; cold colors (green, blue): low focus. AbstractThe fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty. Thirty-six adults walked on a treadmill equipped with a force platform that recorded the positions of the center of pressure. The raw two-dimensional signals were sliced into segments of two gait cycles . A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2,250 segments with 99.9% overall accuracy. A second set of 4,500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used for fine tuning. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures and that CNNs can learn the distinctive features of these trajectories. Using transfer learning, a few strides could be sufficient to learn and identify new gaits.

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Section: Discussionmentioning

confidence: 99%

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Terrier

2020

Applied Sciences

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“…Combined balance and strength training reduce asymmetry of body alignment and can be a more effective balance training method than auditory or tactile feedback ( Ko et al, 2015 ). For persons with a history of falls, rehabilitation programs using foot force plates can improve symmetry while standing ( Scorza et al, 2018 ). In addition, training with asymmetric distribution of body weight improves symmetry standing recovery in older patients ( Cheng et al, 2001 ).…”

Section: Discussionmentioning

confidence: 99%

“…Combined balance and strength training, in which visual feedback is provided, is a treatment program that fully engages the patient during rehabilitation. Laboratory-based devices such as force plates are used for balance and gait assessments and therefore, are informative of biomechanical changes directly affecting an individual’s static and dynamic balance ( Scorza et al, 2018 ). Exercise programs using a force plate allow patients to check their positions and the location of the center of gravity during postural changes in real time, enabling patients to perceive postural information and use it to control and maintain their posture ( Lee and Sun, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of Combined Balance and Strength Training on Measures of Balance and Muscle Strength in Older Women With a History of Falls

Zouita

Ferchichi²,

Paillard

et al. 2020

Front. Physiol.

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ObjectiveWe investigated the effects of combined balance and strength training on measures of balance and muscle strength in older women with a history of falls.MethodsTwenty-seven older women aged 70.4 ± 4.1 years (age range: 65 to 75 years) were randomly allocated to either an intervention (IG, n = 12) or an active control (CG, n = 15) group. The IG completed 8 weeks combined balance and strength training program with three sessions per week including visual biofeedback using force plates. The CG received physical therapy and gait training at a rehabilitation center. Training volumes were similar between the groups. Pre and post training, tests were applied for the assessment of muscle strength (weight-bearing squat [WBS] by measuring the percentage of body mass borne by each leg at different knee flexions [0°, 30°, 60°, and 90°], sit-to-stand test [STS]), and balance. Balance tests used the modified clinical test of sensory interaction (mCTSIB) with eyes closed (EC) and opened (EO), on stable (firm) and unstable (foam) surfaces as well as spatial parameters of gait such as step width and length (cm) and walking speed (cm/s).ResultsSignificant group × time interactions were found for different degrees of knee flexion during WBS (0.0001 < p < 0.013, 0.441 < d < 0.762). Post hoc tests revealed significant pre-to-post improvements for both legs and for all degrees of flexion (0.0001 < p < 0.002, 0.697 < d < 1.875) for IG compared to CG. Significant group × time interactions were found for firm EO, foam EO, firm EC, and foam EC (0.006 < p < 0.029; 0.302 < d < 0.518). Post hoc tests showed significant pre-to-post improvements for both legs and for all degrees of oscillations (0.0001 < p < 0.004, 0.753 < d < 2.097) for IG compared to CG. This study indicates that combined balance and strength training improved percentage distribution of body weight between legs at different conditions of knee flexion (0°, 30°, 60°, and 90°) and also decreased the sway oscillation on a firm surface with eyes closed, and on foam surface (with eyes opened or closed) in the IG.ConclusionThe higher positive effects of training seen in standing balance tests, compared with dynamic tests, suggests that balance training exercises including lateral, forward, and backward exercises improved static balance to a greater extent in older women.

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“…A force plate is the system for measurement of ground reaction force. The evaluation of vertical and propulsion forces and the balance evaluation can be conducted by using ground reaction force [1]. Several force plates are necessary for measurement over multiple strides.…”

Section: Introductionmentioning

confidence: 99%

Motion and Force Analysis in Load Control Type Treadmill Using Wearable Sensor System

Kondo¹,

Hirose

Tsujiuchi

et al. 2020

The 13th Conference of the International Sports Engineering Association

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In this study, we developed a load control type treadmill installed with a force plate and an automatic controller. A conventional treadmill does not measure ground reaction force, and the belt is rotated at a constant speed. The developed system can measure ground reaction force and control the belt speed by feeding back measured propulsion force. A participant can obtain the load by controlling the belt speed corresponding to the target load value. The system provides an environment close to actual motion on a road. We conducted measurement experiments on the treadmill using a wearable motion sensor system in gait and running conditions, and we estimated joint angles in gait condition. The result of the vertical and propulsion force and the joint angles indicated the difference of performance in the levels of the target load value, and the characteristics of load control type treadmill are indicated.

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A Review on Methods and Devices for Force Platforms Calibration in Medical Applications

Cited by 11 publications

References 49 publications

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

Effects of Combined Balance and Strength Training on Measures of Balance and Muscle Strength in Older Women With a History of Falls

Motion and Force Analysis in Load Control Type Treadmill Using Wearable Sensor System

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