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1. Closing the Wearable Gap—Part VIII: A Validation Study for a Smart Knee Brace to Capture Knee Joint Kinematics

   https://doi.org/10.3390/biomechanics1010012  

   Turner, Alana J.; Carroll, Will; Kodithuwakku Arachchige, Sachini N.; Saucier, David; Burch V, Reuben F.; Ball, John E.; Smith, Brian K.; Freeman, Charles E.; Knight, Adam C.; Chander, Harish (June 2021, Biomechanics)

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   Background: Wearable technology is used by clinicians and researchers and play a critical role in biomechanical assessments and rehabilitation. Objective: The purpose of this research is to validate a soft robotic stretch (SRS) sensor embedded in a compression knee brace (smart knee brace) against a motion capture system focusing on knee joint kinematics. Methods: Sixteen participants donned the smart knee brace and completed three separate tasks: non-weight bearing knee flexion/extension, bodyweight air squats, and gait trials. Adjusted R2 for goodness of fit (R2), root mean square error (RMSE), and mean absolute error (MAE) between the SRS sensor and motion capture kinematic data for all three tasks were assessed. Results: For knee flexion/extension: R2 = 0.799, RMSE = 5.470, MAE = 4.560; for bodyweight air squats: R2 = 0.957, RMSE = 8.127, MAE = 6.870; and for gait trials: R2 = 0.565, RMSE = 9.190, MAE = 7.530 were observed. Conclusions: The smart knee brace demonstrated a higher goodness of fit and accuracy during weight-bearing air squats followed by non-weight bearing knee flexion/extension and a lower goodness of fit and accuracy during gait, which can be attributed to the SRS sensor position and orientation, rather than range of motion achieved in each task. 

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2. Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable

   https://doi.org/10.1017/wtc.2023.3  

   Davarzani, Samaneh; Saucier, David; Talegaonkar, Purva; Parker, Erin; Turner, Alana; Middleton, Carver; Carroll, Will; Ball, John E.; Gurbuz, Ali; Chander, Harish; et al (January 2023, Wearable Technologies)

   Abstract The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R -squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system. 

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3. Quality of knee strengthening exercises performed at home deteriorates after one week

   https://doi.org/10.1186/s12891-022-05120-3  

   Mitchell, Ulrike H.; Lee, Hyunwook; Dennis, Hayden E.; Seeley, Matthew K. (December 2022, BMC Musculoskeletal Disorders)

   Abstract Background To compare the performance (as determined by lower extremity kinematics) of knee exercises in healthy middle-aged and older individuals immediately after instruction and one week later. Methods This is a cross-sectional study in a laboratory setting. Nineteen healthy volunteers (age [y] 63.1 ± 8.6, mass [kg] 76.3 ± 14.7, height [m] 1.7 ± 0.1) participated in this study. High speed video and reflective markers were used to track motion during four exercises. The exercises were knee flexion, straight leg raise, and “V “in supine position, and hip abduction in side lying position. All participants received verbal and tactile cues during the training phase and the therapist observed and, if necessary, corrected the exercises. Upon return a week later the participants performed the same exercises without any further instructions. Knee and hip sagittal and rotational angles were extracted from the motion capture. A repeated measures t-test was used to compare the motions between two visits. Results Participants demonstrated more knee flexion during straight leg raise and “V in” exercises at the 2nd visit compared to the 1st visit (both p  <  0.05). During the “V out” exercise, they performed more external rotation ( p  <  0.05) while they showed more internal rotation during the “V in” exercise at the 2nd visit compared to the 1st visit. Conclusions Exercise performance declined significantly in healthy middle-aged and older individuals one week after instruction. This decline occurred despite an instructional exercise sheet being given to every participant. Other approaches designed to help individuals retain the ability to perform rehabilitative exercises correctly need to be explored. 

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4. Closing the Wearable Gap—Part VI: Human Gait Recognition Using Deep Learning Methodologies

   https://doi.org/10.3390/electronics9050796  

   Davarzani, Samaneh; Saucier, David; Peranich, Preston; Carroll, Will; Turner, Alana; Parker, Erin; Middleton, Carver; Nguyen, Phuoc; Robertson, Preston; Smith, Brian; et al (May 2020, Electronics)

   A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well as a cross-sloped surface. In order to evaluate the power of SRS in modeling foot-ankle kinematics, three-dimensional (3D) motion capture data was also collected for analyzing gait movement. Three different approaches were employed to quantify the relationship between the SRS and the 3D motion capture system, including multivariable linear regression, an artificial neural network (ANN), and a time-series long short-term memory (LSTM) network. Models were compared based on the root mean squared error (RMSE) of the prediction of the joint angle of the foot in the sagittal and frontal plane, collected from the motion capture system. There was not a significant difference between the error rates of the three different models. The ANN resulted in an average RMSE of 3.63, being slightly more successful in comparison to the average RMSE values of 3.94 and 3.98 resulting from multivariable linear regression and LSTM, respectively. The low error rate of the models revealed the high performance of SRS in capturing foot-ankle kinematics during the human gait cycle. 

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5. Testing a Quaternion Conversion Method to Determine Human Three-Dimensional Tibiofemoral Angles During an In Vitro Simulated Jump Landing

   https://doi.org/10.1115/1.4052496  

   Ajdaroski, Mirel; Ashton-Miller, James A.; Baek, So Young; Shahshahani, Payam Mirshams; Esquivel, Amanda O. (April 2022, Journal of Biomechanical Engineering)

   Abstract Lower limb joint kinematics have been measured in laboratory settings using fixed camera-based motion capture systems; however, recently inertial measurement units (IMUs) have been developed as an alternative. The purpose of this study was to test a quaternion conversion (QC) method for calculating the three orthogonal knee angles during the high velocities associated with a jump landing using commercially available IMUs. Nine cadaveric knee specimens were instrumented with APDM Opal IMUs to measure knee kinematics in one-legged 3–4× bodyweight simulated jump landings, four of which were used in establishing the parameters (training) for the new method and five for validation (testing). We compared the angles obtained from the QC method to those obtained from a commercially available sensor and algorithm (APDM Opal) with those calculated from an active marker motion capture system. Results showed a significant difference between both IMU methods and the motion capture data in the majority of orthogonal angles (p < 0.01), though the differences between the QC method and Certus system in the testing set for flexion and rotation angles were smaller than the APDM Opal algorithm, indicating an improvement. Additionally, in all three directions, both the limits of agreement and root-mean-square error between the QC method and the motion capture system were smaller than between the commercial algorithm and the motion capture. 

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