This case study explores the problems we solved for a healthcare company that uses machine learning algorithms to improve the motor function of patients with Parkinson’s disease and patients in recovery after experiencing a stroke.
The company selected us due to our expertise in machine learning algorithms.
The case study will summarize the recommendation of therapeutic pathways for patients with Parkinson’s and after strokes, the selection of indicators to monitor, and the description of the competencies that the company’s team needs to be successful.
Recommendation of Therapeutic Pathways for Patients with Parkinson’s and After Strokes
First, we created detailed criteria for a machine learning model that can recommend therapeutic pathways for patients with Parkinson’s and after strokes. These criteria involved gait and balance metrics, correlation analysis between these metrics, selecting a suitable algorithm, designing the model training process, and creating an API for information exchange between the model and medical devices.
- Definition of Gait and Balance Metrics: This stage begins with selecting metrics such as number of steps, heel strike, step length, cadence, stride time, symmetry, variability, and gait speed for model evaluation and API integration.
- Correlation of Metrics with Sound: A correlation analysis between gait/balance metrics and sound should be conducted to identify musical features enhancing motor function. This involves selecting analysis methods, correlating tests, and choosing musical features.
- Designing Requirements for ML Model: The focus is on selecting a suitable machine learning algorithm, conducting comparative experiments, and designing a dedicated algorithm for treatment recommendation based on specific metrics.
- Model Training Process: Once the algorithm is selected, the model will be trained on a representative dataset. This involves data preparation, model training based on stages like data collection, preprocessing, feature extraction, and evaluation.
- API Design: Designing an API to exchange information between the model and medical devices. This involves defining requirements, architecture, development, testing, and ensuring security, privacy, compliance, and usability.
- Other Key Aspects to Consider: Emphasizing security, compliance with regulations like HIPAA and GDPR, and ensuring usability and documentation standards.
Selection of Indicators to Monitor
We also developed iley indicators the healthcare company should monitor, involving the training of a machine learning (ML) model, integration of SoundSteps™ with medical-grade devices, data collection from users and devices, and iterative model refinement.
These indicators include:
- Data Collection and Model Training: Gathering data from users using the SoundSteps prototype in lab settings to train the ML model. Physiotherapists and experts adjust the sound to match the user’s gait.
- Integration and Iteration with Medical Devices: Developing algorithms to connect devices to SoundSteps, testing with healthy subjects, analyzing data for modifications, and iteratively training the ML model using gait sampling videos and paired partner devices.
- Data Collection from Partners: Partner universities collect data from SoundSteps users, analyze it for potential issues, and provide feedback.
- Personalized Playback Configurations: The ML model sends personalized music recommendations to SoundSteps devices. Recommendations are critiqued by partner institutions.
- Expert Tuning of ML Model: Experts in physical and music therapy review video footage and ML model recommendations.
- Infinite Model Training Process: Continuous refinement of the ML model using user feedback.
- Quality Metrics and User Satisfaction: Setting quality metrics for the ML model’s recommendations and aiming for high user satisfaction and improved gait.
Description of the Competencies of the Team Necessary to Carry Out the Task
We also helped the healthcare company develop a list of competencies to develop a team ideal for crafting the machine learning model.
These competencies include:
- Machine Learning and Data Science: Experts in machine learning and data science are crucial for developing, training, and refining the ML model.
- Physiotherapy and Rehabilitation: Physiotherapists play a vital role in understanding gait patterns, assessing user needs, and providing input for sound adjustments to match gait.
- Medical and Therapeutic Expertise: Medical professionals specializing in areas such as orthopedics, neurology, and rehabilitation medicine provide valuable insights into the correlation between gait metrics and therapeutic outcomes.
- Sound Engineering and Music Therapy: Professionals with knowledge in sound engineering and music therapy contribute to designing soundtracks that enhance motor function.
- Software Development and Integration: Skilled software developers are required to integrate SoundSteps with medical-grade devices, design the API, and ensure seamless communication between hardware and software components.
- User Experience (UX) Design: UX designers focus on creating intuitive interfaces for SoundSteps devices and associated applications.
- Regulatory Compliance and Legal Expertise: Professionals with knowledge of healthcare regulations, such as HIPAA and GDPR, ensure that SoundSteps complies with data privacy and security standards.
Conclusion
In conclusion, the collaboration with the healthcare company focused on using machine learning to enhance motor function in Parkinson’s and stroke patients. He recommended personalized treatment pathways and monitoring indicators, emphasizing interdisciplinary expertise for effective model development and implementation, ultimately improving patient care and rehabilitation outcomes.