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Integrating Gait Features into Clinical Decision Support Systems

Elham Rastega, University of Nebraska at Omaha
Hesham Ali, University of Nebraska at Omaha

Description

Gait analysis has become an important clinical tool applied in rehabilitation, sports, diagnosis of medical conditions, and assessment of treatment approaches. Traditional gait analysis systems such as multi-camera motion capture systems and force platforms require expensive facilities and patients’ frequent visit to locomotion laboratories. Furthermore, healthcare is moving rapidly from the long-standing reactive treatment approach to the early detection and preventative era and patients wish to boost their quality of life while the reduced number of physical clinic visits are desired. \ To address the above-mentioned issues and move toward early detection and preventative healthcare, wearable monitoring devices, as alternative gait analysis approach, has been shown to be promising. A great number of experiments using wearables have been successful in capturing and analyzing gait parameters to track the evolution of various diseases. \ There is no doubt that gait analysis can improve clinical decision support systems (CDSS) by improving diagnostic, prognostic, and predictive accuracy; for example, when it comes to diagnosis of Parkinson’s Disease in the early stages or prediction of fall events in the elderly population. However, there are still many challenges for integrating gait into CDSS. Many published prediction models are available, but these models utilize different protocols, different data collection strategies, and they lack standardized assessment of their performance, reproducibility, and clinical utility. Therefore, these models might not be appropriate for CDSS. \ Our ides for this project is to first investigate methodological aspects of developing and validating a clinical diagnosis and prediction model based on wearable-based gait features. our second emphasize will be on the aspects that need to be considered in integrating the model into CDSS. \ In this talk, we describe our proposed comprehensive conceptual framework. We will also point out the challenges, and opportunities of integrating gait to improve CDSS. \

 
Aug 16th, 12:00 AM

Integrating Gait Features into Clinical Decision Support Systems

Gait analysis has become an important clinical tool applied in rehabilitation, sports, diagnosis of medical conditions, and assessment of treatment approaches. Traditional gait analysis systems such as multi-camera motion capture systems and force platforms require expensive facilities and patients’ frequent visit to locomotion laboratories. Furthermore, healthcare is moving rapidly from the long-standing reactive treatment approach to the early detection and preventative era and patients wish to boost their quality of life while the reduced number of physical clinic visits are desired. \ To address the above-mentioned issues and move toward early detection and preventative healthcare, wearable monitoring devices, as alternative gait analysis approach, has been shown to be promising. A great number of experiments using wearables have been successful in capturing and analyzing gait parameters to track the evolution of various diseases. \ There is no doubt that gait analysis can improve clinical decision support systems (CDSS) by improving diagnostic, prognostic, and predictive accuracy; for example, when it comes to diagnosis of Parkinson’s Disease in the early stages or prediction of fall events in the elderly population. However, there are still many challenges for integrating gait into CDSS. Many published prediction models are available, but these models utilize different protocols, different data collection strategies, and they lack standardized assessment of their performance, reproducibility, and clinical utility. Therefore, these models might not be appropriate for CDSS. \ Our ides for this project is to first investigate methodological aspects of developing and validating a clinical diagnosis and prediction model based on wearable-based gait features. our second emphasize will be on the aspects that need to be considered in integrating the model into CDSS. \ In this talk, we describe our proposed comprehensive conceptual framework. We will also point out the challenges, and opportunities of integrating gait to improve CDSS. \