This is the repository for the data collection application portion of the Medical Software Development Module. The task was to utilise a digital biomarker sensor through an android application in order to record patient data, analyse it and transmit it to a backend service.
The field of medicine and biomedical research is undergoing a transformation due to digital biomarkers. Their use is becoming more widespread, with increasing popularity, and the widespread use of wearable technology. Digital biomarkers encompass measurable physiological and behavioral indicators that are collected through portable, wearable, implantable, or digestible digital devices.
The use case and scientific question that digital biomarkers can answer are typically related to providing a prognosis, or to support a diagnosis of a condition. Monitoring a patient recovering from surgery could also be one specific use case. Our project aims to help clinicians monitor the physical well-being of patients through analysing their ability to walk, and to record instances where they may take falls. The application leverages the accelerometer sensor on the android phone in order to detect if an older patient, or a patient with a walking impairment has taken a fall, or if their condition and ability to walk is deteriorating over time. One use case could be the monitoring of a patients gait in order to track their motor function recovery after a stroke for example(1). Monitoring rehabilitation after an operation concerning spinal-cord injuries or lower limb amputations is also a possible use case for gait analysis (2). Patients with hip dysplasia, geriatric disorders, or osteoarthritis could also be monitored to analyse effects of an intervention.
Geriatric patients are especially prone to injury from falls due to frailty, and lower physiological reserve, which affects their ability to walk up stairs or tackle uneven ground(3). Our application focuses on the detection and monitoring of falls that a patient who has a walking impairment, or recovering from a surgery such as a hip replacement (hip prosthesis) may take, and to monitor their condition remotely. In this way the clinician will be more informed about the rehabilitation process, and can make data driven decisions concerning interventions. A key component of the application is the ability to detect falls in real-time. Future versions of the app could implement an alert system. There are two main parts to the applications funtionality, the sensor data collection service and the sensor data collection application.
- Collect sensor data
- Use accelerometer data to detect falls in real time
- Send data to data collection service
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
- Java 8 or higher
- Maven
- Android Studio
- Clone the repository
git clone https://github.com/molinamarcvdb/AndroidAppMSD.git
- Navigate to the project directory
cd AndroidAppMSD - Build the project
gradlew.bat assembleDebug
- Install the application on device or emulator
gradlew.bat installDebug
- Run the application on device or emulator
gradlew.bat runDebug
- Moulaee Conradsson, D., & Bezuidenhout, L. J. (2022). Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke. Sensors (Basel, Switzerland), 22(11), 4080. https://doi.org/10.3390/s22114080
- Jarchi, D., Pope, J., Lee, T. K. M., Tamjidi, L., Mirzaei, A., & Sanei, S. (2018). A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications. IEEE reviews in biomedical engineering, 11, 177–194. https://doi.org/10.1109/RBME.2018.2807182
- Vaishya, R., & Vaish, A. (2020). Falls in Older Adults are Serious. Indian journal of orthopaedics, 54(1), 69–74. https://doi.org/10.1007/s43465-019-00037-x
Marc Molina Van den Bosch, Caterina Montalbano, Lukasz Kaczmarek, Marco De Luca, Paul Stehberger, Paul Tanner
This project is licensed under the MIT License - see the LICENSE.md file for details