Software

Most of software is open-source: For full listing check our GitHub (https://github.com/iupui-soic)

OpenMRS

OpenMRS is an Electronic Medical Record System which was specifically developed curtail the uprising of diseases such as HIV and some of other diseases battling developing nations in the world. Researchers at Purkayastha Laboratory played a pivotal role in the development of various modules of OpenMRS, such as the Radiological Module. We have worked on security testing of OpenMRS.

The research by Purkayastha et al. (2020) published at ICSSA, introduces a formal and continuous approach to security testing for OpenMRS. Recognizing that security is often an afterthought in many digital health applications, their work aims to integrate security testing directly into the software development lifecycle.


The Concept of "Continuous Security"

The research builds upon the concepts of Continuous Integration (CI) and Continuous Deployment (CD), introducing the idea of Continuous Security (CS). Instead of just testing individual components, CS involves deploying the system in a realistic environment and simulating various types of attacks. This allows for the identification and fixing of vulnerabilities during the development process itself, making security an integral part of software development rather than a final, often rushed, step. The project utilizes a popular Python unit testing framework to implement a formal method of security testing. The key components of their methodology include:

  • Behavior-Driven Development (BDD): This approach allows for the creation of testing scripts that can be easily read and understood by both developers and non-technical stakeholders.
  • Gherkin Files: These are simple, English-like files that describe the behavior of the system. They serve a dual purpose: acting as project documentation and automating the tests.
  • Pytest: This is the framework used to run the BDD scripts.
  • Common Vulnerability Scoring System (CVSS): The project uses CVSS to calculate objective metrics that represent the state of security of the deployed system. This provides a clear and consistent way to measure and compare the security of the system after every code change.

mHBS_tracker

This is an android application developed to function as a tracker capture application based on District Health Information System 2(DHIS2). mHBS_tracker is customized for a mobile Helping Babies Survive(mHBS) project. The application leverages on differentDHIS2 packages such as:

  • dhis2-android-sdk
  • dhis2-android-trackercapture
  • dhis2-android-new-sdk
  • dhis2-android-sdk includes the following:
    • Representations of data models such as Data Elements, Events, etc.
    • Existing implementations for connection to DHIS 2 server for retrieving data and sending data.
    • Local persistence for offline support.
    • Services for automatic background synchronization with server.
    • Reusable User Interface elements such as widgets and Login Activity.

For more information on this Click Here

 

FitWisdom App

FitWisdom is a Flutter-based mobile application designed to promote regular physical activity and help users, particularly those with chronic health conditions, develop sustainable exercise habits. The app's core is a novel recommender system, detailed in a recent publication, which addresses critical challenges in health-focused apps like the "cold start" problem and user motivation..

Instead of traditional methods, FitWisdom's recommender system employs a sophisticated two-stage process:

  • Deep Q-Network (DQN), a type of reinforcement learning, generates 2,000 synthetic user profiles. These profiles are created based on clinical guidelines from authoritative sources like the American Heart Association, ensuring they are medically safe and appropriate. This innovative method effectively solves the "cold start" problem without needing initial data from real users.
  • Multi-Armed Contextual Bandit (MAB) algorithm uses these clinically-validated profiles to deliver personalized exercise recommendations. The recommendations are tailored to each user's specific persona, considering their age, comorbidities, and exercise preferences.

A key feature of FitWisdom is its Explainable AI (XAI) layer, which provides transparent justifications for each exercise suggestion. By helping users understand why a particular activity is recommended for their specific health profile, the app aims to increase trust, enhance motivation, and improve long-term adherence to their fitness routines.

Built with Flutter for cross-platform compatibility and Hive for efficient on-device data management, FitWisdom provides tailored, clinically-backed guidance and progress tracking to overcome common barriers to exercise.

For more information Click Here

 

mHBS Trainer

The mobile Helping Babies Survive (mHBS) Training App is a digital health solution designed to empower community health workers with life-saving skills in neonatal resuscitation. Developed through a collaboration between the Indiana University School of Medicine and the Luddy School of Informatics, Computing, and Engineering, this app transforms how essential training is delivered to healthcare workers, especially in resource-limited settings..

Every year, millions of newborns require assistance to begin breathing at birth. The first 60 seconds of life, often called the "Golden Minute," are absolutely critical for intervention. The mHBS app directly addresses this challenge by providing accessible, standardized, and high-quality training based on the evidence-based Helping Babies Survive curriculum. Its primary mission is to equip frontline health workers with the knowledge and practical skills needed to act confidently and effectively during these crucial moments..

To ensure training is both effective and engaging, the mHBS app is equipped with a range of features tailored for adult learners and field environments:

  • Interactive Learning Modules: The app contains step-by-step guides, instructional videos, and simulations that cover the essential principles of neonatal resuscitation.
  • Skill Assessment Checklists: It includes digital checklists for trainers to conduct and record practical skills evaluations, such as Objective Structured Clinical Examinations (OSCEs), ensuring that health workers are competent in performing the required actions.
  • Knowledge Quizzes: Integrated tests and quizzes help reinforce key concepts and allow for the formal assessment of a learner's comprehension.
  • Offline Functionality: Recognizing that internet connectivity can be unreliable in many areas, the app is designed to function fully offline. Health workers can complete training modules and assessments without an active connection, and the data will sync automatically once connectivity is restored.

The app's true strength lies in its backend integration with the District Health Information Software (DHIS2). As a leading open-source health information management platform used by ministries of health worldwide, DHIS2 acts as a robust, centralized repository for all training data captured by the app. Health officials and supervisors can systematically track the progress of individual health workers, monitor course completion rates, and manage certifications on a large scale. Aggregated data provides program managers with valuable insights into training gaps, workforce capacity, and resource needs. This enables them to make informed decisions to strengthen the healthcare system. By leveraging an existing and widely adopted platform like DHIS2, the mHBS training program becomes more sustainable and can be scaled more easily at regional and national levels.

For more information Click Here

 

Human Activity Recognition using ML models on Healthy Subjects and Parkinson's Disease Patients

This Human Activity Recognition (HAR) project utilizes machine learning models to identify and analyze daily living and exercise activities in both healthy individuals and patients with Parkinson's Disease (PD). This innovative system uses advanced ML algorithms to analyze movement patterns, aiming to create a "gold standard" for activity recognition that can be generalized from a healthy population to individuals with PD, whose movements are affected by motor dysfunctions like tremors. The ultimate goal is to improve the monitoring and assessment of Parkinson's Disease by providing objective, quantitative measurements of physical activity, enabling highly personalized exercise recommendations to better manage the disease.

The methodology involved collecting high-precision data with three medically validated BioStamp MC10 sensors placed on the chest, forearm, and calf to capture whole-body movement. These sensors gathered data from accelerometers, gyroscopes, and electrodes, recording motion, rotation, and muscle activity. Data was collected from 8 healthy subjects performing 33 exercises and 8 PD patients performing 21 exercises. The research team then tested four ML models to classify these activities: Random Forest (RF), XGBoost, Long-Short Term Memory (LSTM), and Bidirectional LSTM (BiLSTM).

A primary challenge in this field is generalizability, which is the ability of a model trained on healthy individuals to accurately recognize activities in PD patients. The study's findings showed that while models achieved high accuracy when trained and tested on the same group, with F1-scores between 0.88 and 0.94, their performance dropped significantly when generalized to the other group. This occurs because the models struggle to distinguish intentional movements from involuntary tremors. Despite this, the research successfully identified a core set of high-intensity exercises, such as running and specific boxing movements, that can be reliably predicted across both groups

This project is a significant step toward personalized PD care. By quantifying which high-intensity exercises are measurable, the system provides a foundation for clinicians and health apps to recommend specific activities to help slow disease progression. This tool helps make the process of increasing physical activity more data-driven and precise. To encourage further research, the project has made its dataset and code publicly available for others to develop more robust and generalizable models.

For more information Click Here

 

ECEB (Essential Care for Every Baby) app

The Essential Care for Every Baby (ECEB) mobile application is a clinical decision support tool designed to reduce the cognitive load on healthcare workers in busy, resource-limited settings. As part of the broader mobile Helping Babies Survive (mHBS) initiative, the app empowers nurses, midwives, and other providers by translating essential newborn care guidelines into an accessible, easy-to-use digital format. Its primary goal is to standardize care during the critical first hours and days of a newborn's life, thereby helping to reduce neonatal mortality.

The app provides clear, step-by-step guidance through interactive action plans and clinical workflows. When a nurse is managing multiple patients, the ECEB app serves as a digital job aid, walking them through crucial protocols for routine care, including immediate drying, skin-to-skin contact for warmth, timely initiation of breastfeeding, and infection prevention. This structured support ensures that all critical steps are consistently followed for every baby, even under high-pressure conditions.

To ensure thorough and standardized evaluations, the app incorporates integrated assessment tools and digital checklists. These features guide the healthcare worker through a systematic examination of the newborn to quickly identify the baby's condition and recognize any danger signs that require urgent attention. This helps in making timely and appropriate decisions, improving the quality and safety of care provided.

Designed specifically for challenging environments, the ECEB app includes a rich library of visual aids, such as instructional videos and illustrations, to reinforce training and overcome potential literacy barriers. A crucial feature is its full offline functionality, which ensures it remains a reliable tool without needing a constant internet connection. The app may also include timers for critical time-sensitive actions, further supporting the provider in delivering effective and timely care.

For more information Click Here