Radar-Based Fall Detection for Assisted Living: A Digital-Twin Representation Case Study

Obtaining data on high-impact falls from older adults is ethically difficult, yet these rare events cause many fall-related health problems. As a result, most radar-based fall detectors are trained on staged falls from young volunteers, and representation choices are rarely tested against the radar signals from dangerous falls. This paper uses a frequency-modulated continuous-wave (FMCW) radar digital twin as a single simulated room testbed to study how representation choice affects fall/non-fall discrimination. From the same simulated range-Doppler sequence, Doppler-time spectrograms, three-channel per-receiver spectrogram stacks, and time-pooled range-Doppler maps (RDMs) are derived and fed to an identical compact CNN under matched training on a balanced fall/non-fall dataset. In this twin, temporal spectrograms reach 98-99% test accuracy with similar precision and recall for both classes, while static RDMs reach 89.4% and show more variable training despite using the same backbone. A qualitative comparison between synthetic and measured fall spectrograms suggests that the twin captures gross Doppler-time structure, but amplitude histograms reveal differences in the distributions of amplitude values consistent with receiver processing not modeled in the twin. Because the twin omits noise and hardware impairments and is only qualitatively compared to a single measured example, these results provide representation-level guidance under controlled synthetic conditions rather than ready-to-use clinical performance in real settings.

Indoor Occupancy Classification using a Compact Hybrid Quantum-Classical Model Enabled by a Physics-Informed Radar Digital Twin

Indoor occupancy classification enables privacy-preserving monitoring in settings such as remote elder care, where presence information helps triage alarms without cameras or wearables. Radar suits this role by sensing motion through occlusions and in darkness. Modern deep-learning pipelines are the standard for interpreting radar returns effectively; however, they are often parameter-heavy and sensitive at low signal-to-noise ratios (SNR), motivating compact alternatives like Hybrid Quantum Neural Networks (HQNNs). A two-qubit HQNN is benchmarked against convolutional neural networks (CNNs) using a physics-informed 60GHz digital twin and real radar measurements under matched training protocols. In clean conditions, the HQNN achieves high accuracy (99.7% synthetic; 97.0% real) with up to 170x fewer parameters (0.066M). Its parameter efficiency is shown to be structural, as an ablation of the parameterized quantum circuit (PQC) causes sharp performance drops on real data (to 68.5% and 31.5% for the control heads). A domain-dependent sensitivity emerges under additive-noise evaluation, where the HQNN begins recovery earlier in synthetic data while CNNs recover more steeply and peak higher on real measurements. In label-fraction ablations, CNNs prove more sample-efficient on real Range-Doppler Maps (RDMs), with the performance gap being most pronounced (at 50% labels, BA 0.89-0.99 vs. HQNN 0.75). On synthetic data, this gap narrows significantly, largely vanishing by the 50% label mark. Overall, the HQNN's value lies in parameter efficiency and a compact inductive bias that shapes its distinct sensitivity profile; this work establishes a rigorous baseline for hybrid quantum models in privacy-preserving radar occupancy sensing.