  Machine learning (ML) and deep learning models are extensively used for
parameter optimization and regression problems. However, not all inverse
problems in ML are ``identifiable,'' indicating that model parameters may not
be uniquely determined from the available data and the data model's
input-output relationship. In this study, we investigate the notion of model
parameter identifiability through a case study focused on parameter estimation
from motion sensor data. Utilizing a bipedal-spring mass human walk dynamics
model, we generate synthetic data representing diverse gait patterns and
conditions. Employing a deep neural network, we attempt to estimate
subject-wise parameters, including mass, stiffness, and equilibrium leg length.
The results show that while certain parameters can be identified from the
observation data, others remain unidentifiable, highlighting that
unidentifiability is an intrinsic limitation of the experimental setup,
necessitating a change in data collection and experimental scenarios. Beyond
this specific case study, the concept of identifiability has broader
implications in ML and deep learning. Addressing unidentifiability requires
proven identifiable models (with theoretical support), multimodal data fusion
techniques, and advancements in model-based machine learning. Understanding and
resolving unidentifiability challenges will lead to more reliable and accurate
applications across diverse domains, transcending mere model convergence and
enhancing the reliability of machine learning models.
