arXiv subjects

Sohani Munteha Hiam

Publications and source records attributed to Sohani Munteha Hiam.

3 recordsLinked to original sources

arXiv2026

Confidence-Guided Adaptive Beam Selection for Vision-Aided Beam Tracking in mmWave Systems

Vision-aided beam tracking reduces mmWave beam-training overhead by predicting beam indices from camera observations. Fixed top-$K$ selection, however, assigns the same candidate beam budget to all predictions, ignoring uncertainty variations across samples and horizons. This letter proposes confidence-guided adaptive candidate selection: predictor logits are temperature-calibrated, and the smallest top-ranked beam set whose cumulative probability reaches a target threshold is retained under a maximum budget. On DeepSense~6G Scenario~8, over five random seeds and at matched overhead, the policy improves candidate set accuracy by up to 3.80 percentage points, increases normalized received power by 0.32 percentage points, and reduces outage probability by 2.06 percentage points without retraining the predictor.

eess.SP↗

arXiv2026

Robust Continuous Human Activity Recognition Using Deep Learning and Distributed Radar Sensors

Continuous human activity recognition (HAR) with distributed radar sensor networks is challenging because Doppler signatures depend strongly on aspect angle, the informativeness of individual radar views varies with motion direction, and activity transitions in uninterrupted sequences are often ambiguous. This paper proposes a ConvNeXt-MHSA-BiGRU framework for frame-wise continuous HAR from Doppler-time spectrograms acquired by five spatially distributed radar nodes. A shared ConvNeXt-inspired encoder extracts time-frequency representations from each radar stream, while radar-wise multi-head self attention (MHSA) models inter-node dependencies and adaptively fuses complementary views. RadarDropout and SpecAugment-style time-frequency masking regularize the network against unreliable radar views and local spectrogram perturbations, whereas stacked bidirectional gated recurrent units (BiGRUs) exploit temporal context for frame-wise classification across activity transitions. The framework is evaluated on nine activities performed by 14 participants using leave-one-person-out (L1PO) subject-independent testing with fivefold cross-validation. It achieves a mean L1PO test accuracy of 87.56% across the 14 held-out subjects and improves recognition performance over the published CNN-RNN baseline. The results demonstrate that combining modern spectrogram encoding, adaptive radar view fusion, and bidirectional temporal modeling provides an effective framework for continuous and distributed radar-based HAR.

eess.SP↗

arXiv2024

A Cost-effective, Stand-alone, and Real-time TinyML-Based Gait Diagnosis Unit Aimed at Lower-limb Robotic Prostheses and Exoskeletons

Robotic prostheses and exoskeletons can do wonders compared to their non-robotic counterpart. However, in a cost-soaring world where 1 in every 10 patients has access to normal medical prostheses, access to advanced ones is, unfortunately, extremely limited especially due to their high cost, a significant portion of which is contributed to by the diagnosis and controlling units. However, affordability is often not a major concern for developing such devices as with cost reduction, performance is also found to be deducted due to the cost vs. performance trade-off. Considering the gravity of such circumstances, the goal of this research was to propose an affordable wearable real-time gait diagnosis unit (GDU) aimed at robotic prostheses and exoskeletons. As a proof of concept, it has also developed the GDU prototype which leveraged TinyML to run two parallel quantized int8 models into an ESP32 NodeMCU development board (7.30 USD) to effectively classify five gait scenarios (idle, walk, run, hopping, and skip) and generate an anomaly score based on acceleration data received from two attached IMUs. The developed wearable gait diagnosis stand-alone unit could be fitted to any prosthesis or exoskeleton and could effectively classify the gait scenarios with an overall accuracy of 92% and provide anomaly scores within 95-96 ms with only 3 seconds of gait data in real-time.

cs.RO↗