More Like this

1. WideScan: Exploiting Out-of-Band Distortion for Device Classification Using Deep Learning

   https://doi.org/10.1109/GLOBECOM42002.2020.9348138  

   Elmaghbub, Abdurrahman; Hamdaoui, Bechir; Natarajan, Arun (December 2020, 2020 IEEE Global Communications Conference)

   null (Ed.)

   Wireless device classification techniques play a vital role in supporting spectrum awareness applications, such as spectrum access policy enforcement and unauthorized network access monitoring. Recent works proposed to exploit distortions in the transmitted signals caused by hardware impairments of the devices to provide device identification and classification using deep learning. As technology advances, the manufacturing impairment variations among devices become extremely insignificant, and hence the need for more sophisticated device classification techniques becomes inescapable. This paper proposes a scalable, RF data-driven deep learning-based device classification technique that efficiently classifies transmitting radios from a large pool of bit-similar, high-end, high-performance devices with same hardware, protocol, and/or software configurations. Unlike existing techniques, the novelty of the proposed approach lies in exploiting both the in-band and out-of-band distortion information, caused by inherent hardware impairments, to enable scalable and accurate device classification. Using convolutional neural network (CNN) model for classification, our results show that the proposed technique substantially outperforms conventional approaches in terms of both classification accuracy and learning times. In our experiments, the testing accuracy obtained under the proposed technique is about 96% whereas that obtained under the conventional approach is only about 50% when the devices exhibit very similar hardware impairments. The proposed technique can be implemented with minimum receiver design tuning, as radio technologies, such as cognitive radios, can easily allow for both in-band and out-of band sampling. 

   more » « less
2. PTEase: Objective Airway Examination for Pulmonary Telemedicine using Commodity Smartphones

   https://doi.org/10.1145/3581791.3596854  

   Yin, Xiangyu; Huang, Kai; Forno, Erick; Chen, Wei; Huang, Heng; Gao, Wei (June 2023, Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services)

   Remote monitoring and evaluation of pulmonary diseases via telemedicine are important to disease diagnosis and management, but current telemedicine solutions have limited capability of objectively examining the airway's internal physiological conditions that are crucial to pulmonary disease evaluation. Existing solutions based on smartphone sensing are also limited to externally monitoring breath rates, respiratory events, or lung function. In this paper, we present PTEase, a new system design that addresses these limitations and uses commodity smartphones to examine the airway's internal physiological conditions. PTEase uses active acoustic sensing to measure the internal changes of lower airway caliber, and then leverages machine learning to analyze the sensory data for pulmonary disease evaluation. We implemented PTEase as a smartphone app, and verified its measurement error in lab-controlled settings as <10%. Clinical studies further showed that PTEase reaches 75% accuracy on disease prediction and 11%-15% errors in estimating lung function indices. Given that such accuracy is comparable with that in clinical practice using spirometry, PTEase can be reliably used as an assistive telemedicine tool for disease evaluation and monitoring. 

   more » « less
3. Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants

   https://doi.org/10.3390/electronics11050682  

   Paul, Ankita; Tajin, Md. Abu; Das, Anup; Mongan, William M.; Dandekar, Kapil R. (March 2022, Electronics)

   Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a deep-learning-enabled wearable monitoring system for premature newborn infants, where respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on the infant’s body. We propose a five-stage design pipeline involving data collection and labeling, feature scaling, deep learning model selection with hyperparameter tuning, model training and validation, and model testing and deployment. The model used is a 1-D convolutional neural network (1DCNN) architecture with one convolution layer, one pooling layer, and three fully-connected layers, achieving 97.15% classification accuracy. To address the energy limitations of wearable processing, several quantization techniques are explored, and their performance and energy consumption are analyzed for the respiratory classification task. Results demonstrate a reduction of energy footprints and model storage overhead with a considerable degradation of the classification accuracy, meaning that quantization and other model compression techniques are not the best solution for respiratory classification problem on wearable devices. To improve accuracy while reducing the energy consumption, we propose a novel spiking neural network (SNN)-based respiratory classification solution, which can be implemented on event-driven neuromorphic hardware platforms. To this end, we propose an approach to convert the analog operations of our baseline trained 1DCNN to their spiking equivalent. We perform a design-space exploration using the parameters of the converted SNN to generate inference solutions having different accuracy and energy footprints. We select a solution that achieves an accuracy of 93.33% with 18x lower energy compared to the baseline 1DCNN model. Additionally, the proposed SNN solution achieves similar accuracy as the quantized model with a 4× lower energy. 

   more » « less
4. “They Didn’t Buy Their Smart TV to Watch Me with the Kids”: Comparing Nannies’ and Parents’ Privacy Threat Models for Smart Home Devices

   https://doi.org/10.1145/3702321  

   Abu-Salma, Ruba; Choy, Junghyun; Frik, Alisa; Bernd, Julia (April 2025, ACM Transactions on Computer-Human Interaction)

   Smart home devices raise privacy concerns among not only primary users but also bystanders like domestic workers. We conducted 25 qualitative interviews with nannies and 16 with parents who employed nannies, in the U.S., to explore and compare their views on and privacy threat models for smart home devices. We found device-specific purposes of use inspired different perspectives among nanny participants. Most were comfortable with employers’ smart speakers and smart TVs, whose purpose had nothing to do with them. However, with indoor smart cameras, nanny participants were often not just bystanders but targets of monitoring; in such situations, they had a wider range of attitudes. In contrast, parent participants tended to have more similar views across devices. We found notable disconnects regarding disclosure, where nanny participants often hesitated to ask about cameras, but parent participants assumed nannies just didn’t care. We recommend prioritizing interventions supporting disclosure, discussion, and sharing control. 

   more » « less
5. Smart Bracelets for Remote Monitoring of Wearers' Physical and Affective State

   https://doi.org/10.1109/MECO49872.2020.9134093  

   Rishe, Naphtali David; Adjouadi, Malek; Ortega, Francisco (June 2020, 2020 9th Mediterranean Conference on Embedded Computing (MECO))

   null (Ed.)

   Smart bracelets able to interpret the wearer's emotional state and communicate it to a remote decision-support facility will have broad applications in healthcare, elder care, the military, and other fields. While there are existing commercial embedded devices, such as the Apple Watch, that have health-monitoring sensors, such devices cannot sufficiently support a real-time health-monitoring system with battery-efficient remote data delivery. Ongoing R&D is developing solutions capable of monitoring multiple psycho-physiological signals. Possible hardware configurations include wrist-worn devices and sensors across an augmented reality headset (e.g., HoloLens 2). The device should carry an array of sensors of psycho-physiological signals, including a galvanic skin response sensor, motion sensor, skin temperature sensor, and a heart rate sensor. Output from these sensors can be intelligently fused to monitor the affective state and to determine specific trigger events for the wearer. To enable real-time remote monitoring applications, the device needs to be low-power to allow persistent monitoring while prolonging usage before recharging. For many applications, specialized sensor arrays are required, e.g. a galvanic skin response sensor. An application-flexible device would allow adding/removing sensors and would provide a choice of communication modules (e.g., Bluetooth 5.0 low-energy vs ZigBee). Appropriate configurations of the device would support applications in military health monitoring, drug-addiction mitigation, autistic trigger monitoring, and augmented reality exploration. A configuration example is: motion sensors (3-axis accelerometers, gyroscopes, and magnetometers to track steps, falls, and energy usage), a heart-rate sensor (e.g., an optical-based heart rate sensor with a single monitoring zone using the process of photoplethysmography (PPS)), at least a Bluetooth 5.0 (but a different communication device may be needed depending on the use case), and flash memory to temporarily store data when the device is not remotely communicating. The wearables field has greatly advanced in the quality of sensors; the fusion of multi-sensor data is the current frontier. 

   more » « less