Table 2 The selection criteria of considered AI simulations.
Notation | Criteria | Explanation |
|---|---|---|
\(\:{\varvec{\tau\:}}_{1}\) | Diagnostic Accuracy | Diagnostic Accuracy describes how well the platform detects and diagnoses disability-related conditions by analyzing prosthetic sensor data together with patient movement patterns. The system demonstrates both precise early warning sign detection and reliable consistent diagnostic results regardless of patient profiles. |
\(\:{\varvec{\tau\:}}_{2}\) | Processing Speed | The platform must demonstrate efficient processing of real-time data streams generated by prosthetic devices through Processing Speed. The system’s diagnostic alert response time and its simultaneous processing of multiple data streams together with its speed of delivering actionable insights to healthcare providers form the basis of this criterion. |
\(\:{\varvec{\tau\:}}_{3}\) | Adaptability | Personal health systems demonstrate adaptability through their ability to learn this new data and handle a range of disability types. The platform demonstrates diagnostic flexibility through individualized patient profiling and develops enhanced accuracy by continuously learning from patient interactions and outcome data. |
\(\:{\varvec{\tau\:}}_{4}\) | Integration Capability | The Integration Capability section examines the platform’s ability to connect with current prosthetic hardware along with medical systems. The criterion examines technical support availability together with documentation quality and platform compatibility with prosthetic devices and healthcare information systems. The evaluation takes into account both the simplicity of installation and upkeep within current medical facilities. |
