FDA’s new take on wearables: Moticon Sensor Insoles to assess the efficacy of therapeutic interventions for patients with neurodegenerative disease

FDA’s Office of Science and Engineering Labs has initiated a clinical study aiming at fostering neurodegenerative disease assessment and building an open-access gait database using Moticon’s wireless sensor insoles

Summary

One of the FDA’s Office of Science and Engineering Labs latest clinical studies aims at fostering neurodegenerative disease assessment using Moticon’s wireless sensor insoles and other body worn sensors. The initial validation phase for important gait metrics showed promising outcomes which is now followed by a multi-center clinical data collection trial. Moticon’s OpenGo Sensor Insoles were chosen as a representative technology in the field of professional wearables for medical research and due to a track record of publications related to neurodegenerative diseases (1, 2, 3, 4, 5, 6).

Why the FDA is interested in wearables

The use of wearables as medical devices to monitor changes in gait throughout disease progression and to assess the efficacy of certain therapeutic interventions is one emerging medical technology space that has sparked an interest in researchers at the FDA’s Office of Science and Engineering Labs (OSEL).

Dr. Kimberly Kontson, a principal investigator from OSELs Neurology Research Program, along with a team of investigators, are part of a multi-institutional, longitudinal study with Johns Hopkins Medicine (PI: Dr. Ankur Butala) and the Veterans Health Administration (PI: Dr. Brittney Muir) using body-worn sensors and insole sensors during gait in individuals with neurodegenerative disease.

Challenges in novel gait algorithms to assess neurodegenerative disease progression

FDA’s OSEL is committed to developing regulatory science tools that will expand the scope of innovative science-based approaches to help improve the development and assessment of emerging medical technologies. Pursuant to OSEL’s mission, a primary goal of this study is to develop an open-access dataset that can be used as a regulatory science tool for development and assessment of novel algorithms deriving gait metrics through wearables.

The establishment of this open-access dataset is highly justified to meet several needs simultaneously on a more precise and personalized scale:

  • Testing the capabilities of novel digital signal processing and machine learning technologies
  • Fostering the development of diagnosis support tools, ultimately reducing the time and testing necessary to establish a diagnosis
  • Providing easily reproducible and more objective biomarkers to trend longitudinally in clinical trials

Professional wearables enable open-access gait database development

The study is using the Moticon OpenGo insole pressure sensors as a representative technology for insole pressure measurements. Researchers plan to curate the open-access dataset with raw sensor data from the Moticon insoles and raw inertial measurement unit sensor data (3-axis accelerometry and rotational velocity) synchronized to a gait reference system and associated with clinical evaluations during accurately annotated standardized gait tasks.

Achievements of the validation phase

A preliminary evaluation of concurrent validity of the Moticon sensor insoles to report on spatiotemporal such as stride length, swing time, step time, and double support time was completed and presented at the FDA’s Annual Student Scientific Research Day in August 2023.

The insoles tended to overestimate the spatial parameter of stride length by a mean of 7 cm with 95% limits of agreement (LoA) between -7.4 cm and 21 cm. Swing time was also slightly overestimated by the insoles with a mean bias of 0.08 seconds and LoA between -0.11 and 0.26 seconds. The other temporal parameters of step time and double support time were underestimated by the insoles by 0.05 and 0.15 seconds, respectively. For all measures except swing time, all points fell within the 95% limits of agreement.

Disclaimer

The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.

Dr. Kimberly Konston, PI

Afore listed article was authorized on August 13th, 2023. The following information is provided by Moticon and not part of the authorized article.

Tips for clinical research

The study presented in this story included multi-sensor data collections, which is a frequent setup in clinical research. For such applications, the data generated by Moticon’s OpenGo Sensor Insoles can be combined with other sources of movement analysis such as sensors worn on the upper body. Movella’s IMU sensors are one outstanding example. The study presented here used a prototype version of Moticon’s new OpenGo Sync Box product in order to synchronize OpenGo Sensor Insole data with other sensor systems for validation purposes, which was later transformed by the Moticon development team into a standard OpenGo product.

OpenGo Sync Box

The OpenGo Sync Box is a handy standalone device which connects to the mobile device with the OpenGo App. Specifications include standard TTL voltage signal output on start and stop of a measurement. The next software updates for the product will include a feature for accepting input trigger signals from other MOCAP systems as well as wireless sync options via BLE and Wifi.

References

(1) Tsakanikas, V., et al. 2023. Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data. Sensors, v. 23, issue 8, 10.3390/s23083902. DOI: https://www.mdpi.com/1424-8220/23/8/3902
(2) Channa, A., et al. 2022. Parkinson’s Disease Gait Evaluation Leveraging Wearable Insoles and Deep Learning Approach. 8th Int. CoDIT: Istanbul. DOI: https://ieeexplore.ieee.org/document/9804064
(3) Chatazaki, Ch., et al. 2022. Can Gait Features Help in Differentiating Parkinson’s Disease Medication States and Severity Levels? A Machine Learning Approach. Sensors, v. 22, issue 24, 10.3390/s22249937. DOI: https://www.mdpi.com/1424-8220/22/24/9937
(4) Gatsios, D., et al. 2021. Design of a clinical decision support system powered by mhealth for the management of Parkinson’s disease. 6th SEEDA-CECNSM: Preveza. DOI: https://ieeexplore.ieee.org/document/9566260
(5) Chatzaki, Ch., et al. 2021. The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson’s Patients. Sensors, v. 21, issue 8, 10.3390/s21082821. DOI: https://www.mdpi.com/1424-8220/21/8/2821
(6) Tsakanikas, V. D., et al. 2021. Gait and balance patterns related to Free-Walking and TUG tests in Parkinson’s Disease based on plantar pressure data. 43rd EMBC: Online. DOI: https://ieeexplore.ieee.org/document/9629637

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