ECE Graduate Seminar Lecture Series, Speakers: Mohammad Nur Hossain Khan and Subrata Biswas, PhD Candidates, BASH Lab, ECE Department, WPI

Title:

Mindfulness Meditation and Respiration: Accelerometer-based Respiration Rate and Mindfulness Progress Estimation to Enhance App Engagement and Mindfulness Skills

Speaker:

Mohammad Nur Hossain Khan

PhD Candidate, BASH Lab, ECE Department, WPI

Abstract:

Mindfulness training is widely recognized for its benefits in reducing depression, anxiety, and loneliness. With the rise of smartphone-based mindfulness apps, digital meditation has become more accessible, but sustaining long-term user engagement remains a challenge. This paper explores whether respiration biosignal feedback and mindfulness skill estimation enhance system usability and skill development. We develop a smartphone’s accelerometer-based respiration tracking algorithm, eliminating the need for additional wearables. Unlike existing methods, our approach accurately captures slow breathing patterns typical of mindfulness meditation. Additionally, we introduce the first quantitative framework to estimate mindfulness skills—concentration, sensory clarity, and equanimity—based on accelerometer-derived respiration data. We develop and test our algorithms on 261 mindfulness sessions in both controlled and real-world settings. A user study comparing an experimental group receiving biosignal feedback with a control group using a standard app shows that respiration feedback enhances system usability. Our respiration tracking model achieves a mean absolute error (MAE) of 1.6 breaths per minute, closely aligning with ground truth data, while our mindfulness skill estimation attains F1 scores of 80–84% in tracking skill progression. By integrating respiration tracking and mindfulness estimation into a commercial app, we demonstrate the potential of smartphone sensors to enhance digital mindfulness training.

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Title:

OWL: Geometry-Aware Spatial Reasoning for Audio Large Language Models 

Speaker:

Subrata Biswas

PhD Candidate, BASH Lab, ECE Department, WPI

Abstract:

Spatial reasoning is fundamental to auditory perception, yet current audio large language models (ALLMs) largely rely on unstructured binaural cues and single- step inference. This limits both perceptual accuracy in direction and distance estimation and the capacity for interpretable reasoning. Recent work such as BAT demonstrates spatial QA with binaural audio, but its reliance on coarse categorical labels (left, right, up, down) and the absence of explicit geometric supervision constrain resolution and robustness. We introduce the Spatial-Acoustic Geometry Encoder (SAGE), a geometry-aware audio encoder that aligns binaural acoustic features with 3D spatial structure using panoramic depth images and simulated room-impulse responses at training time, while requiring only audio at inference. Building on this representation, we present OWL, an ALLM that integrates SAGE with a spatially grounded chain-of-thought to rationalize over direction-of-arrivals (DoA) and distance estimates. Through curriculum learning from perceptual QA to multi-step reasoning, OWL supports o’clock-level azimuth and DoA estimation. To enable large-scale training and evaluation, we construct and release BiDepth, a dataset of over one million QA pairs combining binaural audio with panoramic depth images and room impulse responses across both in-room and out-of-room scenarios. Across two benchmark datasets, our new BiDepth and the public SpatialSoundQA, OWL reduces mean DoA error by 11◦ through SAGE and improves spatial reasoning QA accuracy by up to 25% over SOTA baseline.