Foot Strike Classification Using Smartphone Sensor Signals in Lower Extremity Amputee Population

Author(s):  
Pascale Juneau
Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8135
Author(s):  
Sarah Blum ◽  
Daniel Hölle ◽  
Martin Georg Bleichner ◽  
Stefan Debener

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.


2020 ◽  
Vol 186 (11-12) ◽  
pp. e1077-e1087
Author(s):  
Erin M Miller ◽  
Michael S Crowell ◽  
Jamie B Morris ◽  
John S Mason ◽  
Rebeca Zifchock ◽  
...  

Abstract Introduction Running-related musculoskeletal injury (RRI) among U.S. military service members continues to negatively impact force readiness. There is a paucity of evidence supporting the use of RRI interventions, such as gait retraining, in military populations. Gait retraining has demonstrated effectiveness in altering running biomechanics and reducing running load. The purpose of this pilot study was to investigate the clinical effect of a gait retraining intervention on a military cadet population recovering from a lower-extremity RRI. Materials and Methods The study design is a pilot study. Before study initiation, institutional approval was granted by the Keller Army Community Hospital Office of Human Research Protections. Nine rearfoot strike (RFS) runners recovering from a lower-extremity RRI at the U.S. Military Academy were prospectively enrolled and completed a gait retraining intervention. Participants followed-up with their assigned medical provider 6 times over 10 weeks for a clinical evaluation and running gait retraining. Gait retraining was provided utilizing verbal, visual, and audio feedback to facilitate a change in running foot strike pattern from RFS to non-rearfoot strike (NRFS) and increase preferred running step rate. At pre-intervention and post-intervention running ground reaction forces (GRF) [average vertical loading rate (AVLR), peak vertical GRF], kinematic (foot strike pattern) and temporospatial (step rate, contact time) data were collected. Participants self-reported their level of function via the Single Assessment Numeric Evaluation, Patient-Specific Functional Scale, and total weekly running minutes. Paired samples t-tests and Wilcoxon signed rank tests were used to compare pre- and post-intervention measures of interest. Values of P < .05 were considered statistically significant. Results Nine patients completed the 10-week intervention (age, 20.3 ± 2.2 years; height, 170.7 ± 13.8 cm; mass, 71.7 ± 14.9 kg; duration of injury symptoms, 192.4 ± 345.5 days; running speed, 2.8 ± 0.38 m/s). All nine runners (100%) transitioned from RFS to NRFS. Left AVLR significantly decreased from 60.3 ± 17.0 bodyweight per second (BW/s) before intervention to 25.9 ± 9.1 BW/s after intervention (P = 0.008; effect size (d) = 2.5). Right AVLR significantly decreased from 60.5 ± 15.7 BW/s to 32.3 ± 12.5 BW/s (P < .001; d = 2.0). Similarly, step rate increased from 169.9 ± 10.0 steps per minute (steps/min) before intervention to 180.5 ± 6.5 steps/min following intervention (P = .005; d = 1.3). Single Assessment Numeric Evaluation scores improved significantly from 75 ± 23 to 100 ± 8 (P = .008; d = 1.5) and Patient-Specific Functional Scale values significantly improved from 6 ± 2.3 to 9.5 ± 1.6 (P = .007; d = 1.8) after intervention. Peak vertical GRF (left, P = .127, d = 0.42; right, P = .052, d = 0.53), contact time (left, P = 0.127, d = 0.42; right, P = 0.052, d = 0.53), and total weekly continuous running minutes (P = 0.095, d = 0.80) remained unchanged at post-intervention. All 9 patients remained injury free upon a 6-month medical record review. Conclusions In 9 military service members with a RRI, a 10-week NRFS gait retraining intervention was effective in improving running mechanics and measures of function. Patients remained injury-free 6 months following enrollment. The outcomes of this pilot study suggest that individuals recovering from certain lower-extremity RRIs may benefit from transitioning to an NRFS running pattern.


1994 ◽  
Vol 84 (4) ◽  
pp. 171-180 ◽  
Author(s):  
KM Knutzen ◽  
A Price

Twenty nonsymptomatic subjects were assessed while walking at a photoelectronically monitored place (2 +/- 0.1 m.s-1) using high speed cinematography (200 Hz) to record the rearfoot motion in the frontal plane, and electrogoniometry (100 Hz) to measure joint kinematics in the lower extremity. The foot type of the subjects was determined statically by using a podiascope and digitization techniques. The results demonstrated that no foot type variables contributed significantly to the variance in either rearfoot angle at foot strike or maximum rearfoot angle (p > 0.05). Regression equations were developed using kinematic variables: rearfoot angle at foot strike = 3.81 + (0.06*time to hip internal rotation) - (0.46*tibia internal rotation) + (0.14*plantarflexion); (R = 0.87, SE = 1.23 degrees); maximum rearfoot angle = 4.02 + (0.52*hip internal rotation) - (0.11*time to hip internal rotation); (R = 0.66, SE = 2.07 degrees). This study identifies hip joint movements as being the most significant contributors to prediction of rearfoot angles produced during walking.


2015 ◽  
pp. 150219105224004 ◽  
Author(s):  
Donald L. Goss ◽  
Michael Lewek ◽  
Bing Yu ◽  
William B. Ware ◽  
Deydre S. Teyhen ◽  
...  

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3298 ◽  
Author(s):  
Reginaldo K. Fukuchi ◽  
Claudiane A. Fukuchi ◽  
Marcos Duarte

Background The goals of this study were (1) to present the set of data evaluating running biomechanics (kinematics and kinetics), including data on running habits, demographics, and levels of muscle strength and flexibility made available at Figshare (DOI: 10.6084/m9.figshare.4543435); and (2) to examine the effect of running speed on selected gait-biomechanics variables related to both running injuries and running economy. Methods The lower-extremity kinematics and kinetics data of 28 regular runners were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes. Results A dataset comprising raw and processed kinematics and kinetics signals pertaining to this experiment is available in various file formats. In addition, a file of metadata, including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. Overall, there was an effect of running speed on most of the gait-biomechanics variables selected for this study. However, the foot-strike patterns were not affected by running speed. Discussion Several applications of this dataset can be anticipated, including testing new methods of data reduction and variable selection; for educational purposes; and answering specific research questions. This last application was exemplified in the study’s second objective.


2019 ◽  
Vol 35 (1) ◽  
pp. 87-90
Author(s):  
Ricardo Pires ◽  
Thays Falcari ◽  
Alexandre B. Campo ◽  
Bárbara C. Pulcineli ◽  
Joseph Hamill ◽  
...  

2017 ◽  
Author(s):  
Reginaldo K Fukuchi ◽  
Claudiane A Fukuchi ◽  
Marcos Duarte

Background. The goals of this study were (1) to present the set of data evaluating running biomechanics (kinematics and kinetics), including data on running habits, demographics, and levels of muscle strength and flexibility made available at Figshare (DOI: 10.6084/m9.figshare.4543435); and (2) to examine the effect of running speed on selected gait-biomechanics variables related to both running injuries and running economy. Methods. The lower-extremity kinematics and kinetics data of 28 regular runners were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes. Results. A dataset comprising raw and processed kinematics and kinetics signals pertaining to this experiment is available in various file formats. In addition, a file of metadata, including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. Overall, there was an effect of running speed on most of the gait-biomechanics variables selected for this study. However, the foot-strike patterns were not affected by running speed. Discussion. Several applications of this dataset can be anticipated, including testing new methods of data reduction and variable selection; for educational purposes; and answering specific research questions. This last application was exemplified in the study’s second objective.


2017 ◽  
Author(s):  
Reginaldo K Fukuchi ◽  
Claudiane A Fukuchi ◽  
Marcos Duarte

Background. The goals of this study were (1) to present the set of data evaluating running biomechanics (kinematics and kinetics), including data on running habits, demographics, and levels of muscle strength and flexibility made available at Figshare (DOI: 10.6084/m9.figshare.4543435); and (2) to examine the effect of running speed on selected gait-biomechanics variables related to both running injuries and running economy. Methods. The lower-extremity kinematics and kinetics data of 28 regular runners were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes. Results. A dataset comprising raw and processed kinematics and kinetics signals pertaining to this experiment is available in various file formats. In addition, a file of metadata, including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. Overall, there was an effect of running speed on most of the gait-biomechanics variables selected for this study. However, the foot-strike patterns were not affected by running speed. Discussion. Several applications of this dataset can be anticipated, including testing new methods of data reduction and variable selection; for educational purposes; and answering specific research questions. This last application was exemplified in the study’s second objective.


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