THE ECONOMICS OF INNOVATION IN THE PROSTHETIC AND ORTHOTICS INDUSTRY

Innovation is an important part of the prosthetic and orthotics (P&amp;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&amp;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&amp;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.&#x0D; Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/35203/28318&#x0D; How To Cite: Andrysek J. The economics of innovation in the prosthetic and orthotics industry. Canadian Prosthetics &amp; Orthotics Journal. 2021; Volume 4, Issue 2, No.7. https://doi.org/10.33137/cpoj.v4i2.35203&#x0D; Corresponding Author: Jan Andrysek, PhDHolland Bloorview Kids Rehabilitation Hospital, Toronto, Canada.E-Mail: [email protected];  [email protected] ID: https://orcid.org/0000-0002-4976-1228</jats:p

THE ECONOMICS OF INNOVATION IN THE PROSTHETIC AND ORTHOTICS INDUSTRY

Innovation is an important part of the prosthetic and orthotics (P&amp;O) industry.&nbsp; 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&amp;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&amp;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 &amp; 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]; &nbsp;[email protected] ID: https://orcid.org/0000-0002-4976-122

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

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

Hidden Markov model-based similarity measure (HMM-SM) for gait quality assessment of lower-limb prosthetic users using inertial sensor signals

Abstract Background Gait quality indices, such as the Gillette Gait Index or Gait Profile Score (GPS), can provide clinicians with objective, straightforward measures to quantify gait pathology and monitor changes over time. However, these methods often require motion capture or stationary gait analysis systems, limiting their accessibility. Inertial sensors offer a portable, cost-effective alternative for gait analysis. This study aimed to evaluate a novel hidden Markov model-based similarity measure (HMM-SM) for assessing gait quality directly from gyroscope and accelerometer data captured by inertial sensors. Methods Walking trials were conducted with 26 lower-limb prosthetic users and 30 able-bodied individuals, using inertial sensors placed at various lower body locations. We computed the HMM-SM score along with other established inertial sensor-based methods, including the Movement Deviation Profile, Dynamic Time Warping, IMU-based Gait Normalcy Index, and Multifeature Gait Score. Spearman correlations with the GPS, a validated measure of gait quality, were assessed, as well as correlations among the inertial sensor methods. Welch’s t-tests were used to evaluate the ability to distinguish between prosthetic subgroups. Results The HMM-SM and other inertial sensor-based methods demonstrated moderate-to-strong correlations with the GPS (0.49 <|r|< 0.77 for significant correlations). Comparisons between different measures highlighted key similarities and differences, both in correlations and in their ability to differentiate between subgroups. Overall, the pelvis and lower leg sensors achieved significant correlations and outperformed the upper leg sensors, which did not achieve significant correlations with the GPS for any of the signal-based measures. Conclusion Results suggest inertial sensors located at the pelvis and lower leg provide valid markers for monitoring overall gait quality, offering the potential to develop nonobtrusive, wearable systems to facilitate long-term monitoring. Such systems could enhance rehabilitation by enabling continuous gait assessment that can be easily integrated in clinical and everyday settings

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

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.</jats:p

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