arXiv subjects

Sandip Chakraborty

Publications and source records attributed to Sandip Chakraborty.

At least 19 recordsLinked to original sources

arXiv2026

PoHAR: Understanding Hyperlocal Human Activities with Pollution Sensor Networks

Low-cost air quality sensors are becoming ubiquitous in our daily lives as public awareness of air pollution continues to grow, and people take measures to monitor and improve the air they breathe indoors. Besides the standard operation of these sensors, fluctuations in environmental parameters can be leveraged to understand human behavior and activities in indoor spaces. Unlike traditional audio-visual, Radio Frequency, and inertial sensors, air quality sensors are easily scalable to a household, are privacy-preserving, and more economical. Such distributed sensor networks must jointly make decisions to monitor indoor occupants for downstream smart home and healthcare applications. However, due to low processing power, memory, and energy, they often struggle to maintain distributed data consensus and identify activity-affected sensor groups for accurate on-device inference. In this paper, we propose PoHAR framework that implements: (i) a conflict-free replicated data primitive for data sharing, (ii) a hierarchical clustering for ESP32 to detect activity-affected sensor groups with a self-supervised distance metric, and (iii) a leader-based group inference with off-the-shelf ML classifiers, enabling the sensor network to collaboratively detect hyperlocal indoor activities. Our extensive experiments demonstrated on-device activity detection, achieving 97.41% accuracy for indoor activity and 99.68% for cooking activity, using off-the-shelf ML models with latency below 34 microseconds.

cs.DC↗

arXiv2026

MIRO: Multi-radar Identity and Ranging for Occupational Safety

Occupational exposure to airborne particulate matter (PM) poses a severe health risk in open industrial workspaces such as stonecutting yards. Conventional monitoring solutions such as wearable PM sensors and camera-based tracking are impractical due to discomfort, maintenance issues, and privacy concerns. We present MIRO, a privacy-preserving framework that integrates continuous PM sensing with a multi-radar millimeter-wave (mmWave) re-identification (re-ID) backbone. A distributed network of PM sensors captures localized pollutant concentrations, while spatially overlapping mmWave radars track and re-associate workers across viewpoints without relying on visual cues. To ensure identity consistency across radars, we introduce a GAN-based view adaptation network that compensates for azimuthal distortions in range-Doppler (RD) signatures, combined with correlation-based cross-radar matching. In controlled laboratory experiments, our system achieves a re-ID F1-score of 90.4% and a mean Structural Similarity Index Measure (SSIM) of 0.70 for view adaptation accuracy. Field trials in rural stone-cutting yards further validate the system's robustness, demonstrating reliable worker-specific PM exposure estimation.

cs.HC↗

arXiv2026

From Invisible to Actionable: Augmented Reality Interactions with Indoor CO2

Indoor carbon dioxide (CO2) can rapidly accumulate to form invisible pollution hotspots, posing significant health risks due to its odorless and colorless nature. Despite growing interest in wearable or stationary sensors for pollutant detection, effectively visualizing CO2 levels and engaging individuals remains an ongoing challenge. In this paper, we develop a portable wrist-sized pollution sensor that detects CO2 in real time at any indoor location and reveals CO2 bubbles by highlighting sudden spikes. In order to promote better ventilation habits and user awareness, we also develop a smartphone-based augmented reality (AR) game for users to locate and disperse these high-CO2 zones. A user study with 35 participants demonstrated increased engagement and heightened understanding of CO2's health impacts. Our system's usability evaluations yielded a median score of 1.88, indicating its strong practicality.

cs.HC↗

arXiv2025

VibraWave: Sensing the Pulse of Polluted Waters

Conventional methods for water pollutant detection, such as chemical assays and optical spectroscopy, are often invasive, expensive, and unsuitable for real-time, portable monitoring. In this paper, we introduce VibraWave, a novel non-invasive sensing framework that combines mmWave radar with controlled acoustic excitation, tensor decomposition, and deep learning to detect and quantify a wide range of water pollutants. By capturing radar reflections as a three-dimensional tensor encoding phase dynamics, range bin power, and angle-of-arrival (AoA), we apply PARAFAC decomposition with non-negative constraints to extract compact, interpretable pollutant fingerprints. These are used to train a lightweight student neural network via knowledge distillation, enabling joint classification and quantification of heavy metals (Cu, Fe, Mg), oil emulsions, and sediments. Extensive experiments show that VibraWave achieves high accuracy and low RMSE across pure, binary, and tertiary mixtures, while remaining robust and computationally efficient, making it well-suited for scalable, real-time water quality monitoring.

eess.SY↗

arXiv2025

Auditable Ledger Snapshot for Non-Repudiable Cross-Blockchain Communication

Blockchain interoperability is increasingly recognized as the centerpiece for robust interactions among decentralized services. Blockchain ledgers are generally tamper-proof and thus enforce non-repudiation for transactions recorded within the same network. However, such a guarantee does not hold for cross-blockchain transactions. When disruptions occur due to malicious activities or system failures within one blockchain network, foreign networks can take advantage by denying legitimate claims or mounting fraudulent liabilities against the defenseless network. In response, this paper introduces InterSnap, a novel blockchain snapshot archival methodology, for enabling auditability of crossblockchain transactions, enforcing non-repudiation. InterSnap introduces cross-chain transaction receipts that ensure their irrefutability. Snapshots of ledger data along with these receipts are utilized as non-repudiable proof of bilateral agreements among different networks. InterSnap enhances system resilience through a distributed snapshot generation process, need-based snapshot scheduling process, and archival storage and sharing via decentralized platforms. Through a prototype implementation based on Hyperledger Fabric, we conducted experiments using on-premise machines, AWS public cloud instances, as well as a private cloud infrastructure. We establish that InterSnap can recover from malicious attacks while preserving crosschain transaction receipts. Additionally, our proposed solution demonstrates adaptability to increasing loads while securely transferring snapshot archives with minimal overhead.

cs.CR↗

arXiv2025

Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications

Accurate and adaptive network throughput prediction is essential for latency-sensitive and bandwidth-intensive applications in 5G and emerging 6G networks. However, most existing methods rely on centralized training with uniformly collected data, limiting their applicability in heterogeneous mobile environments with non-IID data distributions. This paper presents the first comprehensive benchmarking of federated learning (FL) strategies for throughput prediction in realistic 5G edge scenarios. We evaluate three aggregation algorithms - FedAvg, FedProx, and FedBN - across four time-series architectures: LSTM, CNN, CNN+LSTM, and Transformer, using five diverse real-world datasets. We systematically analyze the effects of client heterogeneity, cohort size, and history window length on prediction performance. Our results reveal key trade-offs among model complexities, convergence rates, and generalization. It is found that FedBN consistently delivers robust performance under non-IID conditions. On the other hand, LSTM and Transformer models outperform CNN-based baselines by up to 80% in R2 scores. Moreover, although Transformers converge in half the rounds of LSTM, they require longer history windows to achieve a high R2, indicating higher context dependence. LSTM is, therefore, found to achieve a favorable balance between accuracy, rounds, and temporal footprint. To validate the end-to-end applicability of the framework, we have integrated our FL-based predictors into a live adaptive streaming pipeline. It is seen that FedBN-based LSTM and Transformer models improve mean QoE scores by 11.7% and 11.4%, respectively, over FedAvg, while also reducing the variance. These findings offer actionable insights for building scalable, privacy-preserving, and edge-aware throughput prediction systems in next-generation wireless networks.

cs.DC↗

arXiv2025

RadarTrack: Enhancing Ego-Vehicle Speed Estimation with Single-chip mmWave Radar

In this work, we introduce RadarTrack, an innovative ego-speed estimation framework utilizing a single-chip millimeter-wave (mmWave) radar to deliver robust speed estimation for mobile platforms. Unlike previous methods that depend on cross-modal learning and computationally intensive Deep Neural Networks (DNNs), RadarTrack utilizes a novel phase-based speed estimation approach. This method effectively overcomes the limitations of conventional ego-speed estimation approaches which rely on doppler measurements and static surrondings. RadarTrack is designed for low-latency operation on embedded platforms, making it suitable for real-time applications where speed and efficiency are critical. Our key contributions include the introduction of a novel phase-based speed estimation technique solely based on signal processing and the implementation of a real-time prototype validated through extensive real-world evaluations. By providing a reliable and lightweight solution for ego-speed estimation, RadarTrack holds significant potential for a wide range of applications, including micro-robotics, augmented reality, and autonomous navigation.

cs.RO↗

arXiv2025

Generation of Optimized Solidity Code for Machine Learning Models using LLMs

While a plethora of machine learning (ML) models are currently available, along with their implementation on disparate platforms, there is hardly any verifiable ML code which can be executed on public blockchains. We propose a novel approach named LMST that enables conversion of the inferencing path of an ML model as well as its weights trained off-chain into Solidity code using Large Language Models (LLMs). Extensive prompt engineering is done to achieve gas cost optimization beyond mere correctness of the produced code, while taking into consideration the capabilities and limitations of the Ethereum Virtual Machine. We have also developed a proof of concept decentralized application using the code so generated for verifying the accuracy claims of the underlying ML model. An extensive set of experiments demonstrate the feasibility of deploying ML models on blockchains through automated code translation using LLMs.

cs.ET↗

arXiv2024

Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities

In recent years, indoor air pollution has posed a significant threat to our society, claiming over 3.2 million lives annually. Developing nations, such as India, are most affected since lack of knowledge, inadequate regulation, and outdoor air pollution lead to severe daily exposure to pollutants. However, only a limited number of studies have attempted to understand how indoor air pollution affects developing countries like India. To address this gap, we present spatiotemporal measurements of air quality from 30 indoor sites over six months during summer and winter seasons. The sites are geographically located across four regions of type: rural, suburban, and urban, covering the typical low to middle-income population in India. The dataset contains various types of indoor environments (e.g., studio apartments, classrooms, research laboratories, food canteens, and residential households), and can provide the basis for data-driven learning model research aimed at coping with unique pollution patterns in developing countries. This unique dataset demands advanced data cleaning and imputation techniques for handling missing data due to power failure or network outages during data collection. Furthermore, through a simple speech-to-text application, we provide real-time indoor activity labels annotated by occupants. Therefore, environmentalists and ML enthusiasts can utilize this dataset to understand the complex patterns of the pollutants under different indoor activities, identify recurring sources of pollution, forecast exposure, improve floor plans and room structures of modern indoor designs, develop pollution-aware recommender systems, etc.

cs.LG↗

arXiv2024

Exploiting Air Quality Monitors to Perform Indoor Surveillance: Academic Setting

Changing public perceptions and government regulations have led to the widespread use of low-cost air quality monitors in modern indoor spaces. Typically, these monitors detect air pollutants to augment the end user's understanding of her indoor environment. Studies have shown that having access to one's air quality context reinforces the user's urge to take necessary actions to improve the air over time. Thus, user's activities significantly influence the indoor air quality. Such correlation can be exploited to get hold of sensitive indoor activities from the side-channel air quality fluctuations. This study explores the odds of identifying eight indoor activities (i.e., enter, exit, fan on, fan off, AC on, AC off, gathering, eating) in a research lab with an in-house low-cost air quality monitoring platform named DALTON. Our extensive data collection and analysis over three months shows 97.7% classification accuracy in our dataset.

cs.HC↗

arXiv2024

A Dataset for Multi-intensity Continuous Human Activity Recognition through Passive Sensing

Human activity recognition (HAR) is essential in healthcare, elder care, security, and human-computer interaction. The use of precise sensor data to identify activities passively and continuously makes HAR accessible and ubiquitous. Specifically, millimeter wave (mmWave) radar is promising for passive and continuous HAR due to its ability to penetrate non-metallic materials and provide high-resolution wireless sensing. Although mmWave sensors are effective at capturing macro-scale activities, like exercising, they fail to capture micro-scale activities, such as typing. In this paper, we introduce mmDoppler, a novel dataset that utilizes off-the-shelf (COTS) mmWave radar in order to capture both macro and micro-scale human movements using a machine-learning driven signal processing pipeline. The dataset includes seven subjects performing 19 distinct activities and employs adaptive doppler resolution to enhance activity recognition. By adjusting the radar's doppler resolution based on the activity type, our system captures subtle movements more precisely. mmDoppler includes range-doppler heatmaps, offering detailed motion dynamics, with data collected in a controlled environment with single as well as multiple subjects performing activities simultaneously. The dataset aims to bridge the gap in HAR systems by providing a more comprehensive and detailed resource for improving the robustness and accuracy of mmWave radar activity recognition.

cs.HC↗

arXiv2024

Exploring Indoor Air Quality Dynamics in Developing Nations: A Perspective from India

Indoor air pollution is a major issue in developing countries such as India and Bangladesh, exacerbated by factors like traditional cooking methods, insufficient ventilation, and cramped living conditions, all of which elevate the risk of health issues like lung infections and cardiovascular diseases. With the World Health Organization associating around 3.2 million annual deaths globally to household air pollution, the gravity of the problem is clear. Yet, extensive empirical studies exploring these unique patterns and indoor pollutions extent are missing. To fill this gap, we carried out a six months long field study involving over 30 households, uncovering the complexity of indoor air pollution in developing countries, such as the longer lingering time of VOCs in the air or the significant influence of air circulation on the spatiotemporal distribution of pollutants. We introduced an innovative IoT air quality sensing platform, the Distributed Air QuaLiTy MONitor (DALTON ), explicitly designed to meet the needs of these nations, considering factors like cost, sensor type, accuracy, network connectivity, power, and usability. As a result of a multi-device deployment, the platform identifies pollution hot-spots in low and middle-income households in developing nations. It identifies best practices to minimize daily indoor pollution exposure. Our extensive qualitative survey estimates an overall system usability score of 2.04, indicating an efficient system for air quality monitoring.

cs.HC↗

arXiv2024

Dynamic Ego-Velocity estimation Using Moving mmWave Radar: A Phase-Based Approach

Precise ego-motion measurement is crucial for various applications, including robotics, augmented reality, and autonomous navigation. In this poster, we propose mmPhase, an odometry framework based on single-chip millimetre-wave (mmWave) radar for robust ego-motion estimation in mobile platforms without requiring additional modalities like the visual, wheel, or inertial odometry. mmPhase leverages a phase-based velocity estimation approach to overcome the limitations of conventional doppler resolution. For real-world evaluations of mmPhase we have developed an ego-vehicle prototype. Compared to the state-of-the-art baselines, mmPhase shows superior performance in ego-velocity estimation.

cs.RO↗

arXiv2024

Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data

Traditional human-in-the-loop-based annotation for time-series data like inertial data often requires access to alternate modalities like video or audio from the environment. These alternate sources provide the necessary information to the human annotator, as the raw numeric data is often too obfuscated even for an expert. However, this traditional approach has many concerns surrounding overall cost, efficiency, storage of additional modalities, time, scalability, and privacy. Interestingly, recent large language models (LLMs) are also trained with vast amounts of publicly available alphanumeric data, which allows them to comprehend and perform well on tasks beyond natural language processing. Naturally, this opens up a potential avenue to explore LLMs as virtual annotators where the LLMs will be directly provided the raw sensor data for annotation instead of relying on any alternate modality. Naturally, this could mitigate the problems of the traditional human-in-the-loop approach. Motivated by this observation, we perform a detailed study in this paper to assess whether the state-of-the-art (SOTA) LLMs can be used as virtual annotators for labeling time-series physical sensing data. To perform this in a principled manner, we segregate the study into two major phases. In the first phase, we investigate the challenges an LLM like GPT-4 faces in comprehending raw sensor data. Considering the observations from phase 1, in the next phase, we investigate the possibility of encoding the raw sensor data using SOTA SSL approaches and utilizing the projected time-series data to get annotations from the LLM. Detailed evaluation with four benchmark HAR datasets shows that SSL-based encoding and metric-based guidance allow the LLM to make more reasonable decisions and provide accurate annotations without requiring computationally expensive fine-tuning or sophisticated prompt engineering.

cs.LG↗

arXiv2024

"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living

Complex activities of daily living (ADLs) often consist of multiple micro-activities. When performed sequentially, these micro-activities help the user accomplish the broad macro-activity. Naturally, a deeper understanding of these micro-activities can help develop more sophisticated human activity recognition (HAR) models and add explainability to their inferred conclusions. Previous research has attempted to achieve this by utilizing fine-grained annotated data that provided the required supervision and rules for associating the micro-activities to identify the macro-activity. However, this ``bottom-up'' approach is unrealistic as getting such high-quality, fine-grained annotated sensor datasets is challenging, costly, and time-consuming. Understanding this, in this paper, we develop AmicroN, which adapts a ``top-down'' approach by exploiting coarse-grained annotated data to expand the macro-activities into their constituent micro-activities without any external supervision. In the backend, AmicroN uses \textit{unsupervised} change-point detection to search for the micro-activity boundaries across a complex ADL. Then, it applies a \textit{generalized zero-shot} approach to characterize it. We evaluate AmicroN on two real-life publicly available datasets and observe that AmicroN can identify the micro-activities with micro F\textsubscript{1}-score $>0.75$ for both datasets. Additionally, we also perform an initial proof-of-concept on leveraging the state-of-the-art (SOTA) large language models (LLMs) with attribute embeddings predicted by AmicroN to enhance further the explainability surrounding the detection of micro-activities.

cs.HC↗

arXiv2023

Exploring Indoor Health: An In-depth Field Study on the Indoor Air Quality Dynamics

Indoor air pollution, a significant driver of respiratory and cardiovascular diseases, claims 3.2 million lives yearly, according to the World Health Organization, highlighting the pressing need to address this global crisis. In contrast to unconstrained outdoor environments, room structures, floor plans, ventilation systems, and occupant activities all impact the accumulation and spread of pollutants. Yet, comprehensive in-the-wild empirical studies exploring these unique indoor air pollution patterns and scope are lacking. To address this, we conducted a three-month-long field study involving over 28 indoor spaces to delve into the complexities of indoor air pollution. Our study was conducted using our custom-built DALTON air quality sensor and monitoring system, an innovative IoT air quality monitoring solution that considers cost, sensor type, accuracy, network connectivity, power, and usability. Our study also revealed that conventional measures, such as the Indoor Air Quality Index (IAQI), don't fully capture complex indoor air quality dynamics. Hence, we proposed the Healthy Home Index (HHI), a new metric considering the context and household activities, offering a more comprehensive understanding of indoor air quality. Our findings suggest that HHI provides a more accurate air quality assessment, underscoring the potential for wide-scale deployment of our indoor air quality monitoring platform.

cs.HC↗

arXiv2023

Continuous Multi-user Activity Tracking via Room-Scale mmWave Sensing

Continuous detection of human activities and presence is essential for developing a pervasive interactive smart space. Existing literature lacks robust wireless sensing mechanisms capable of continuously monitoring multiple users' activities without prior knowledge of the environment. Developing such a mechanism requires simultaneous localization and tracking of multiple subjects. In addition, it requires identifying their activities at various scales, some being macro-scale activities like walking, squats, etc., while others are micro-scale activities like typing or sitting, etc. In this paper, we develop a holistic system called MARS using a single Commercial off the-shelf (COTS) Millimeter Wave (mmWave) radar, which employs an intelligent model to sense both macro and micro activities. In addition, it uses a dynamic spatial time sharing approach to sense different subjects simultaneously. A thorough evaluation of MARS shows that it can infer activities continuously with a weighted F1-Score of > 94% and an average response time of approx 2 sec, with 5 subjects and 19 different activities.

cs.HC↗

arXiv2023

mmDrive: mmWave Sensing for Live Monitoring and On-Device Inference of Dangerous Driving

Detecting dangerous driving has been of critical interest for the past few years. However, a practical yet minimally intrusive solution remains challenging as existing technologies heavily rely on visual features or physical proximity. With this motivation, we explore the feasibility of purely using mmWave radars to detect dangerous driving behaviors. We first study characteristics of dangerous driving and find some unique patterns of range-doppler caused by 9 typical dangerous driving actions. We then develop a novel Fused-CNN model to detect dangerous driving instances from regular driving and classify 9 different dangerous driving actions. Through extensive experiments with 5 volunteer drivers in real driving environments, we observe that our system can distinguish dangerous driving actions with an average accuracy of > 95%. We also compare our models with existing state-of-the-art baselines to establish their significance.

cs.HC↗