Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis.

OBJECTIVE: To compare the accuracy and reliability of 10 different accelerometer-based step-counting algorithms for individuals with lower limb loss, accounting for different clinical characteristics and real-world activities. DESIGN: Cross-sectional study. SETTING: General community setting (ie, institutional research laboratory and community free-living). PARTICIPANTS: Forty-eight individuals with a lower limb amputation (N=48) wore an ActiGraph (AG) wGT3x-BT accelerometer proximal to the foot of their prosthetic limb during labeled indoor/outdoor activities and community free-living. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Intraclass correlation coefficient (ICC), absolute and root mean square error (RMSE), and Bland Altman plots were used to compare true (manual) step counts to estimated step counts from the proprietary AG Default algorithm and low frequency extension filter, as well as from 8 novel algorithms based on continuous wavelet transforms, fast Fourier transforms (FFTs), and peak detection. RESULTS: All algorithms had excellent agreement with manual step counts (ICC>0.9). The AG Default and FFT algorithms had the highest overall error (RMSE=17.81 and 19.91 steps, respectively), widest limits of agreement, and highest error during outdoor and ramp ambulation. The AG Default algorithm also had among the highest error during indoor ambulation and stairs, while a FFT algorithm had the highest error during stationary tasks. Peak detection algorithms, especially those using pre-set parameters with a trial-specific component, had among the lowest error across all activities (RMSE=4.07-8.99 steps). CONCLUSIONS: Because of its simplicity and accuracy across activities and clinical characteristics, we recommend the peak detection algorithm with set parameters to count steps using a prosthetic-worn AG among individuals with lower limb loss for clinical and research applications.

Evaluation of Robotic-Assisted Carotid Artery Stenting in a Virtual Model Using Motion-Based Performance Metrics.

PURPOSE: Robotic-assisted carotid artery stenting (CAS) cases have been demonstrated with promising results. However, no quantitative measurements have been made to compare manual with robotic-assisted CAS. This study aims to quantify surgical performance using tool tip kinematic data and metrics of precision during CAS with manual and robotic control in an ex vivo model. MATERIALS AND METHODS: Transfemoral CAS cases were performed in a high-fidelity endovascular simulator. Participants completed cases with manual and robotic techniques in 2 different carotid anatomies in random order. C-arm angulations, table position, and endovascular devices were standardized. Endovascular tool tip kinematic data were extracted. We calculated the spectral arc length (SPARC), average velocity, and idle time during navigation in the common carotid artery and lesion crossing. Procedural time, fluoroscopy time, movements of the deployed filter wire, precision of stent, and balloon positioning were recorded. Data were analyzed and compared between the 2 modalities. RESULTS: Ten participants performed 40 CAS cases with a procedural success of 100% and 0% residual stenosis. The median procedural time was significantly higher during the robotic-assisted cases (seconds, median [interquartile range, IQR]: 128 [49.5] and 161.5 [62.5], p=0.02). Fluoroscopy time differed significantly between manual and robotic-assisted procedures (seconds, median [IQR]: 81.5 [32] and 98.5 [39.5], p=0.1). Movement of the deployed filter wire did not show significant difference between manual and robotic interventions (mm, median [IQR]: 13 [10.5] and 12.5 [11], p=0.5). The postdilation balloon exceeded the margin of the stent with a median of 2 [1] mm in both groups. Navigation with robotic assistance showed significantly lower SPARC values (-5.78±3.14 and -8.63±3.98, p=0.04) and higher idle time values (8.92±8.71 and 3.47±3.9, p=0.02) than those performed manually. CONCLUSIONS: Robotic-assisted and manual CAS cases are comparable in the precision of stent and balloon positioning. Navigation in the carotid artery is associated with smoother motion and higher idle time values. These findings highlight the accuracy and the motion stabilizing capability of the endovascular robotic system. CLINICAL IMPACT: Robotic assistance in the treatment of peripheral vascular disease is an emerging field and may be a tool for radiation protection and the geographic distribution of endovascular interventions in the future. This preclinical study compares the characteristics of manual and robotic-assisted carotid stenting (CAS). Our results highlight, that robotic-assisted CAS is associated with precise navigation and device positioning, and smoother navigation compared to manual CAS.

Comparing Manual and Robotic-Assisted Carotid Artery Stenting Using Motion-Based Performance Metrics.

Carotid artery stenting (CAS) is a minimally invasive endovascular procedure used to treat carotid artery disease and is an alternative treatment option for carotid artery stenosis. Robotic assistance is becoming increasingly widespread in these procedures and can provide potential benefits over manual intervention, including decreasing peri- and post-operative risks associated with CAS. However, the benefits of robotic assistance in CAS procedures have not been quantitatively verified at the level of surgical tool motions. In this work, we compare manual and robot-assisted navigation in CAS procedures using performance metrics that reliably indicate surgical navigation proficiency. After extracting guidewire tip motion profiles from recorded procedure videos, we computed spectral arc length (SPARC), a frequency-domain metric of movement smoothness, average guidewire velocity, and amount of idle tool motion (idle time) for a set of CAS procedures performed on a commercial endovascular surgical simulator. We analyzed the metrics for two procedural steps that influence post-operative outcomes. Our results indicate that during advancement of the sheath to the distal common carotid artery, there are significant differences in SPARC (F(1, 22.3) = 6.12, p = .021) and idle time (F(1, 22.6) = 6.26, p = .02) between manual and robot-assisted navigation, as well as a general trend of lower SPARC, lower average velocity, and higher idle time values associated with robot-assisted navigation for both procedural steps. Our findings indicate that significant differences exist between manual and robot-assisted CAS procedures. These are quantitatively detectable at the granular-level of physical tool motion, improving the ability to evaluate robotic assistance as it grows in clinical use.