Load-Bearing Detection with Insole-Force Sensors Provides New Treatment Insights in Fragility Fractures of the Pelvis

Pfeufer, Daniel; Becker, C.; Faust, Leon; Keppler, Alexander Martin; Stagg, Marissa; Kammerlander, Christian; Böcker, Wolfgang; Neuerburg, Carl

doi:10.3390/jcm9082551

Cited by 11 publications

(15 citation statements)

References 27 publications

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“…Loading of the lower limbs. [9] Six original flexible piezoresistive pressure sensors attached to the lower surface of the universal shoe lining.…”

Section: Analysis Of Gait Phases Durationmentioning

confidence: 99%

“…Various systems were used for human gait analysis: 3-D-acceleration sensors with data logger and a computer program for processing the acceleration signals and calculating the gait parameters [ 2 ]; Vicon 3D motion capture system, with 10 digital cameras and the reflective markers for measuring the movements of the segments of lower extremities, and two sequentially staggered AMTI force platforms measuring ground reaction forces [ 8 ]; insole-force sensors used for gait analysis and wireless plantar force measurements in hopping, walking, and running [ 9 ]; Vicon 370 system with strobe cameras, force plates, reflective markers, and Vicon Clinical Manager software used for gait analysis, and the handheld dynamometer for the muscle strength [ 10 ]. These methods are effective and accurate; however, they are too expensive and complicated to use in everyday activities.…”

Section: Introductionmentioning

confidence: 99%

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Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis

Bučinskas

Dzedzickis

Rožėnė

et al. 2021

Sensors

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Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat®-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person.

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“…Loading of the lower limbs. [9] Six original flexible piezoresistive pressure sensors attached to the lower surface of the universal shoe lining.…”

Section: Analysis Of Gait Phases Durationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis

Bučinskas

Dzedzickis

Rožėnė

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…A new approach to assess physical activity and gait parameters includes the use of wearables and physical activity monitors [13]. These devices enable physicians and researchers to assess physical activity comprehensively under real-life conditions, and they have already been successfully applied to assist in the diagnosis of musculoskeletal diseases and to monitor rehabilitation [14][15][16][17]. A more recent development is sensor insoles with pressure and gyroscope sensors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study

Kraus¹,

Saller²,

Baumbach³

et al. 2022

JMIR Med Inform

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Background Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making. Objective The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F: strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms. Methods This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical frailty were assessed, including body composition, questionnaires (European Quality of Life 5-dimension [EQ 5D 5L], SARC-F), and physical performance tests (SPPB, TUG), along with digital sensor insole gait parameters collected during the TUG test. Physical frailty was defined as an SPPB score≤8. Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty. Results Classified by the SPPB, 23 of the 57 eligible patients were defined as having physical frailty. Several gait parameters were significantly different between the two groups (with and without physical frailty). The area under the receiver operating characteristic curve (AUROC) of the TUG test was superior to that of the SARC-F (0.862 vs 0.639). The recursive feature elimination algorithm identified 9 parameters, 8 of which were digital insole gait parameters. Both the KNN and RF algorithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively). Conclusions A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients.

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Section: Introductionmentioning

confidence: 99%

Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study (Preprint)

Kraus¹,

Saller²,

Baumbach³

et al. 2021

Preprint

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BACKGROUND Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making. OBJECTIVE The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F: strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms. METHODS This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical frailty were assessed, including body composition, questionnaires (European Quality of Life 5-dimension [EQ 5D 5L], SARC-F), and physical performance tests (SPPB, TUG), along with digital sensor insole gait parameters collected during the TUG test. Physical frailty was defined as an SPPB score≤8. Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty. RESULTS Classified by the SPPB, 23 of the 57 eligible patients were defined as having physical frailty. Several gait parameters were significantly different between the two groups (with and without physical frailty). The area under the receiver operating characteristic curve (AUROC) of the TUG test was superior to that of the SARC-F (0.862 vs 0.639). The recursive feature elimination algorithm identified 9 parameters, 8 of which were digital insole gait parameters. Both the KNN and RF algorithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively). CONCLUSIONS A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients.

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Load-Bearing Detection with Insole-Force Sensors Provides New Treatment Insights in Fragility Fractures of the Pelvis

Cited by 11 publications

References 27 publications

Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis

Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis

Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study

Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study (Preprint)

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