THE ECONOMICS OF INNOVATION IN THE PROSTHETIC AND ORTHOTICS INDUSTRY

Innovation is an important part of the prosthetic and orthotics (P&O) industry.  Innovation has the potential to improve health care services and outcomes, however, it can also be a burden to the system if misdirected. This paper explores the interaction of innovation and economics within the P&O industry, focusing on its current state and future opportunities. Technological advancement, industry competition and pursuit of better patient outcomes drive innovation, while challenges in ensuring better P&O health care include lagging clinical evidence, limited access to data, and existing funding structures. There exists a greater need for inclusive models and frameworks for rehabilitation care, that focus on the use of appropriate technology as supported by research and evidence of effectiveness and cost-effectiveness. Additionally, innovative business models based on social entrepreneurism could open access to untapped and underserved markets and provide greater access to assistive technology. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/35203/28318 How To Cite: Andrysek J. The economics of innovation in the prosthetic and orthotics industry. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.7. https://doi.org/10.33137/cpoj.v4i2.35203 Corresponding Author: Jan Andrysek, PhDHolland Bloorview Kids Rehabilitation Hospital, Toronto, Canada.E-Mail: [email protected];  [email protected] ID: https://orcid.org/0000-0002-4976-122

Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor Signals

Wearable sensors allow for the objective analysis of gait and motion both in and outside the clinical setting. However, it remains a challenge to apply such systems to highly diverse patient populations, including individuals with lower-limb amputations (LLA) that present with unique gait deviations and rehabilitation goals. This paper presents the development of a novel method using continuous gyroscope data from a single inertial sensor for person-specific classification of gait changes from a physiotherapist-led gait training session. Gyroscope data at the thigh were collected using a wearable gait analysis system for five LLA before, during, and after completing a gait training session. Data from able-bodied participants receiving no intervention were also collected. Models using dynamic time warping (DTW) and Euclidean distance in combination with the nearest neighbor classifier were applied to the gyroscope data to classify the pre- and post-training gait. The model achieved an accuracy of 98.65% ± 0.69 (Euclidean) and 98.98% ± 0.83 (DTW) on pre-training and 95.45% ± 6.20 (Euclidean) and 94.18% ± 5.77 (DTW) on post-training data across the participants whose gait changed significantly during their session. This study provides preliminary evidence that continuous angular velocity data from a single gyroscope could be used to assess changes in amputee gait. This supports future research and the development of wearable gait analysis and feedback systems that are adaptable to a broad range of mobility impairments

Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking

Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains

New Hands, New Life: Robots, Prostheses, and Innovation

https://stars.library.ucf.edu/diversefamilies/3238/thumbnail.jp

Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor Signals

Wearable sensors allow for the objective analysis of gait and motion both in and outside the clinical setting. However, it remains a challenge to apply such systems to highly diverse patient populations, including individuals with lower-limb amputations (LLA) that present with unique gait deviations and rehabilitation goals. This paper presents the development of a novel method using continuous gyroscope data from a single inertial sensor for person-specific classification of gait changes from a physiotherapist-led gait training session. Gyroscope data at the thigh were collected using a wearable gait analysis system for five LLA before, during, and after completing a gait training session. Data from able-bodied participants receiving no intervention were also collected. Models using dynamic time warping (DTW) and Euclidean distance in combination with the nearest neighbor classifier were applied to the gyroscope data to classify the pre- and post-training gait. The model achieved an accuracy of 98.65% ± 0.69 (Euclidean) and 98.98% ± 0.83 (DTW) on pre-training and 95.45% ± 6.20 (Euclidean) and 94.18% ± 5.77 (DTW) on post-training data across the participants whose gait changed significantly during their session. This study provides preliminary evidence that continuous angular velocity data from a single gyroscope could be used to assess changes in amputee gait. This supports future research and the development of wearable gait analysis and feedback systems that are adaptable to a broad range of mobility impairments

THE SHORT-TERM EFFECTS OF RHYTHMIC VIBROTACTILE AND AUDITORY BIOFEEDBACK ON THE GAIT OF INDIVIDUALS AFTER WEIGHT-INDUCED ASYMMETRY

BACKGROUND: Biofeedback (BFB), the practice of providing real-time sensory feedback has been shown to improve gait rehabilitation outcomes. BFB training through rhythmic stimulation has the potential to improve spatiotemporal gait asymmetries while minimizing cognitive load by encouraging a synchronization between the user’s gait cycle and an external rhythm. OBJECTIVE: The purpose of this work was to evaluate if rhythmic stimulation can improve the stance time symmetry ratio (STSR) and to compare vibrotactile to auditory stimulation. Gait parameters including velocity, cadence, stride length, double support time, and step length symmetry, were also examined. METHODOLOGY: An experimental rhythmic stimulation system was developed, and twelve healthy adults (5 males), age 28.42 ± 10.93 years, were recruited to participate in walking trials. A unilateral ankle weight was used to induce a gait asymmetry to simulate asymmetry as commonly exhibited by individuals with lower limb amputation and other clinical disorders. Four conditions were evaluated: 1) No ankle weight baseline, 2) ankle weight without rhythmic stimulation, 3) ankle weight + rhythmic vibrotactile stimulation (RVS) using alternating motors and 4) ankle weight + rhythmic auditory stimulation (RAS) using a single-tone metronome at the participant’s self-selected cadence. FINDINGS: As expected the STSR became significantly more asymmetrical with the ankle weight (i.e. induced asymmetry condition). STSR improved significantly with RVS and RAS when compared to the ankle weight without rhythmic stimulation. Cadence also significantly improved with RVS and RAS compared to ankle weight without rhythmic stimulation. With the exception of double support time, the other gait parameters were unchanged from the ankle weight condition. There were no statistically significant differences between RVS and RAS. CONCLUSION: This study found that rhythmic stimulation can improve the STSR when an asymmetry is induced. Moreover, RVS is at least as effective as auditory stimulation in improving STSR in healthy adults with an induced gait asymmetry. Future work should be extended to populations with mobility impairments and outside of laboratory settings. Layman's Abstract Providing feedback to users in real-time has been shown to improve walking in many populations with gait deviations. Feedback in the form of rhythmic stimulation involves consistent cues to which the user matches their movement. This work compared the effects of sound-based (RAS) and vibration-based (RVS) stimulation systems on the walking symmetry of healthy adults. A simple stimulation system was used with twelve healthy adults in walking trials. The walking trials included some in which the participant wore an ankle weight on a single leg to create a non-symmetrical walking pattern. Four different conditions were tested: No ankle weight, with an ankle weight, with an ankle weight and RAS, and with an ankle weight and RVS. Walking symmetry improved with both RVS and RAS compared to ankle weight only. Walking speed, cadence, and step length did not change. These findings show that RVS is at least as effective as RAS and may be a useful technique for gait rehabilitation. Future work should involve clinical populations and in real-world settings. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36223/29090 How To Cite: Michelini A., Sivasambu H., Andrysek J. The short-term effects of rhythmic vibrotactile and auditory biofeedback on the gait of individuals after weight-induced asymmetry. Canadian Prosthetics & Orthotics Journal. 2022; Volume 5, Issue 1, No.6. https://doi.org/10.33137/cpoj.v5i1.36223 Corresponding Author: Jan Andrysek, PhDBloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada.E-Mail: [email protected] ORCID ID: https://orcid.org/0000-0002-4976-122

Exploring the Tactor Configurations of Vibrotactile Feedback Systems for Use in Lower-Limb Prostheses

Vibrotactile feedback may be able to compensate for the loss of sensory input in lower-limb prosthesis users to improve the mobility function. Designing an effective vibrotactile feedback system requires that users are able to perceive and respond to vibrotactile stimuli correctly and in a timely manner. Our study explored four key tactor configuration variables (i.e., tactors’ prosthetic layer, vibration intensity, prosthetic pressure, and spacing between adjacent tactors) through two experiments. The vibration propagation experiment investigated the effects of tactor configurations on vibration amplitude at the prosthesis–limb interface. Results revealed a positive relationship between vibration amplitude and intensity and a weak relationship between vibration amplitude and prosthetic pressure. Highest vibration amplitudes were observed when the tactor was located on the inner socket layer. The second experiment involving a sample of ten able-bodied and three amputee subjects investigated the effects of tactor configurations on user perception measured by response time, accuracy identifying tactors’ stimulation patterns, and spatial error in locating the tactors. Results showed that placing the tactors on the inner socket layer, greater spacing between adjacent tactors, and higher vibration intensity resulted in better user perception. The above findings can be directly applied to the design of vibrotactile feedback systems to increase the user response accuracy and decrease the response time required for dynamic tasks such as gait. They can also help to inform future clinical trials informing the optimization of tactor configuration variables

Local interpretability analysis for two samples wearing ATK prosthesis using the four models.

P1 and P2 are randomly selected study participants.</p

Hyperparameters obtained for the classifiers.

Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters. Nonetheless, explainable machine learning provides an alternative technique for distinguishing the significant and influential gait changes stemming from a given intervention. The goal of this work was to demonstrate the use of explainable ML models in gait analysis for prosthetic rehabilitation in both population- and sample-based interpretability analyses. Models were developed to classify amputee gait with two types of prosthetic knee joints. Sagittal plane gait patterns of 21 individuals with unilateral transfemoral amputations were video-recorded and 19 spatiotemporal and kinematic gait parameters were extracted and included in the models. Four ML models—logistic regression, support vector machine, random forest, and LightGBM—were assessed and tested for accuracy and precision. The Shapley Additive exPlanations (SHAP) framework was applied to examine global and local interpretability. Random Forest yielded the highest classification accuracy (98.3%). The SHAP framework quantified the level of influence of each gait parameter in the models where knee flexion-related parameters were found the most influential factors in yielding the outcomes of the models. The sample-based explainable ML provided additional insights over the population-based analyses, including an understanding of the effect of the knee type on the walking style of a specific sample, and whether or not it agreed with global interpretations. It was concluded that explainable ML models can be powerful tools for the assessment of gait-related clinical interventions, revealing important parameters that may be overlooked using conventional statistical methods.</div