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A novel sensorized shoe system to classify gait severity in children with cerebral palsy
Chiara Mancinelli1, Shyamal Patel, Lynn C Deming
1Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston MA 02114, USA. cmancinelli@partners.org
Insights
A new wearable sensor system, ActiveGait, enables frequent home-based monitoring of gait deviations in children with Cerebral Palsy (CP). This technology provides accurate, real-world data to improve clinical management and intervention effectiveness.
Area of Science:
- Biomedical Engineering
- Pediatric Neurology
- Rehabilitation Technology
Background:
- Clinical management of Cerebral Palsy (CP) requires regular gait deviation assessments.
- Current clinical assessments are infrequent and limited to clinical settings, hindering real-world impact evaluation.
- Home-based quantitative gait assessments are needed for effective intervention feedback and clinical scheduling.
Purpose of the Study:
- To introduce ActiveGait, a novel sensorized shoe-based system for monitoring gait deviations in children with CP.
- To assess the feasibility and accuracy of home-based gait deviation monitoring using wearable technology.
- To develop a methodology for deriving gait deviation severity measures from sensor data.
Main Methods:
- Developed ActiveGait, a wearable, sensorized shoe system for gait data collection.
- Collected data from 11 children with CP under supervised and unsupervised home conditions.
- Utilized Center of Pressure (CoP) trajectory features to derive gait deviation severity measures.
- Employed a Random Forest classifier to estimate severity scores.
Main Results:
- The ActiveGait system successfully gathered gait data in a home setting.
- A methodology was established to derive severity measures from CoP trajectories.
- The Random Forest classifier achieved >80% accuracy in estimating severity scores based on the Edinburgh Visual Scale.
- Results indicate suitability for clinical use.
Conclusions:
- ActiveGait offers a viable solution for frequent, home-based monitoring of gait deviations in children with CP.
- This technology can provide valuable, real-world data to enhance clinical decision-making and intervention strategies.
- Accurate, automated severity scoring supports personalized rehabilitation and improved patient outcomes.
Abstract:
The clinical management of children with Cerebral Palsy (CP) relies upon periodic assessments of changes in the severity of gait deviations in response to clinical interventions. Current clinical practice is limited to sporadic assessments in a clinical environment and hence it is limited in its ability to estimate the impact of CP-related gait deviations in real-life conditions. Frequent home-based quantitative assessments of the severity of gait deviations would be extremely useful in scheduling clinical visits and gathering feedback about the effectiveness of intervention strategies. The use of a wearable system would allow clinicians to gather information about the severity of gait deviations in the home setting. In this paper, we present ActiveGait, a novel sensorized shoe-based system for monitoring gait deviations. The ActiveGait system was used to gather data, under supervised and unsupervised conditions, from a group of 11 children with various levels of CP-related gait deviation severities. We present a methodology to derive severity measures based on features extracted from Center of Pressure (CoP) trajectories. Results show that a Random Forest classifier is able to estimate severity scores based on the Edinburgh Visual Scale with a level of accuracy >80% adequate for clinical use.
