About Me

I am a Senior Research Scientist in the Pervasive Systems Department at Nokia Bell Labs in Cambridge, and a visitor at the University of Cambridge.
My research interests revolve around the architectural and algorithmic challenges of building practical wearable systems for human sensing, exploiting ultra-low-power machine learning techniques. In particular, I lead the team's efforts in earable computing (esense.io) and batteryless sensing. Furthermore, I serve as a committee member for leading mobile and sensor systems conferences and journals.

I am an experimental computer scientist driven by solving important problems with practical solutions, hence, my work always involves building end-to-end HW and SW systems and evaluating them in real-world deployments. I strongly believe in an interdisciplinary approach to research and I am always looking for opportunities to contribute to the development of new technologies.

I finished my PhD in Computer Science at the University of Cambridge (UK) under the supervision of Prof. Cecilia Mascolo. My PhD focused on wearable devices to study social dynamics. I also hold an MEng and a BEng cum laude in Computer Engineering from the University of Bologna (Italy).

During my studies, I have worked on a number of projects in diverse industries, including: wireless sensor networks at the ABB Corporate Research Center, privacy issues in the context of the Internet of Things at Nokia Bell Labs, and Internet of Things applications at ARM. I was also a Research Assistant at the University of Cambridge working on optimising drone surveying flight paths using onboard context sensing and computation.

Selected Projects

Logic-based Intelligence for Batteryless Sensors

ACM HotMobile 2022

In this project we look beyond traditional arithmetic-based neural networks (NNs) to the logic-based learning algorithm called the Tsetlin Machine (TM). We argue that the Tsetlin Machine's simple architecture makes them a promising candidate for batteryless ML systems despite the substantial memory footprint of trained models. To solve this issue, we propose a lossless compression scheme based on run-length encoding and evaluate against standard TMs for vision and acoustic workloads. Our encoding can compress the model by up to 99% without accuracy loss, translating into lower memory footprint and better energy efficiency.

In-Ear PPG for Vital Signs

IEEE Pervasive Computing 2021

We report a systematic characterization of in-ear photoplethysmography (PPG) in measuring vital signs: heart rate, heart rate variability, blood oxygen saturation, and respiration rate. We explore in-ear PPG inaccuracies stemming from different sensor placements and motion-induced artifacts. We explore in-ear PPG inaccuracies stemming from different sensor placements and motion-induced artifacts. We observe statistically significant differences across sensor placements and between artifact types. Our results suggest that in-ear PPG is reasonably accurate in detecting vital signs but demands careful mechanical design and signal processing treatment.

ePerceptive—Energy Reactive Embedded Intelligence for Batteryless Sensors

ACM SenSys 2020

This work presents the design and implementation of ePerceptive: a novel framework for best-effort embedded intelligence, i.e., inference fidelity which varies in proportion to the instantaneous energy supplied. We report the manifestation of ePerceptive in designing batteryless cameras and microphones built with TI MSP430 MCU and off-the-shelf RF and solar energy harvesters. Our evaluation with multiple vision and acoustic workloads suggest that the dynamic adaptation of ePerceptive can increase the inference throughput by up to 80% compared to a static baseline while ensuring a maximum accuracy drop of less than 6%.

Automatic Smile and Frown Recognition with Kinetic Earables

ACM Augmented Human 2019

In this work, we introduce inertial signals obtained from an earable placed in the ear canal as a new compelling sensing modality for recognising two key facial expressions: smile and frown. Borrowing principles from Facial Action Coding Systems, we first demonstrate that an inertial measurement unit of an earable can capture facial muscle deformation activated by a set of temporal microexpressions. Building on these observations, we then present three different learning schemes - shallow models with statistical features, hidden Markov model, and deep neural networks to automatically recognise smile and frown expressions from inertial signals. The results show that in controlled non-conversational settings, we can identify smile and frown with high accuracy (F1 score: 0.85).

Measuring Interaction Proxemics with Wearable Light Tags

ACM IMWUT 2018

We present Protractor, a novel wearable technology for measuring interaction proxemics as part of non-verbal behavior cues with finne granularity. Protractor employs near-infrared light to monitor both the distance and relative body orientation of interacting users. We fabricated Protractor tags and conducted real-world experiments. Results show its accuracy in tracking body distances and relative angles. The framework achieves less than 6 error 95% of the time for measuring relative body orientation and 2.3-cm – 4.9-cm mean error in estimating interaction distance. We deployed Protractor tags to track user’s non-verbal behaviors among 64 participants. Results show that distance and angle data from can help assess individual’s task role with 84.9% accuracy, and identify task timeline with 93.2% accuracy.

A Study of Bluetooth Low Energy Performance for Human Proximity Detection in the Workplace

IEEE PerCom 2017

We present an extensive evaluation of Bluetooth Low Energy as a technology to monitor people proximity in the workplace. We examine the key parameters that affect the accuracy of the detected contacts and their impact on power consumption. We study how this system can be implemented on popular wearable devices and the resulting limitations. Through a real world deployment in a commercial organisation with 25 participants we evaluate the performances of a BLE-based proximity detection technique. Our results show the suitability of BLE for workplace interaction detection and give guidance to vendors and Operating System (OS) developers on the impact of the restrictions regarding the use of BLE on commodity wearables.

Publications

Selected Peer-reviewed Publications

  • Logic-based Intelligence for Batteryless Sensors
    Abu Bakar, Tousif Rahman, Alessandro Montanari, Jie Lei, Rishad Shafik, Fahim Kawsar
    23rd International Workshop on Mobile Computing Systems and Applications (HotMobile 2022), March 2022
    Publisher PDF
  • In-Ear PPG for Vital Signs
    Andrea Ferlini, Alessandro Montanari, Chulhong Min, Hongwei Li, Ugo Sassi, Fahim Kawsar
    IEEE Pervasive Computing, December 2021
    Publisher PDF
  • FatigueSet: A Multi-modal Dataset for Modeling Mental Fatigue and Fatigability
    Manasa Kalanadhabhatta, Chulhong Min, Alessandro Montanari, Fahim Kawsar
    EAI International Conference on Pervasive Computing Technology for Healthcare (PervasiveHealth 2021), December 2021.
    PDF Dataset
  • Enabling In-Ear Magnetic Sensing: Automatic and User Transparent Magnetometer Calibration
    Andrea Ferlini, Alessandro Montanari, Andreas Grammenos, Robert Harle, Cecilia Mascolo
    IEEE International Conference on Pervasive Computing and Communications (PerCom 2021), March 2021.
    Publisher PDF
  • ePerceptive: Energy Reactive Embedded Intelligence for Batteryless Sensors
    Alessandro Montanari, Manuja Sharma, Dainius Jenkus, Mohammed Alloulah, Lorena Qendro, Fahim Kawsar
    18th Conference on Embedded Networked Sensor Systems (SenSys 2020), November 2020.
    Publisher PDF
  • A Closer Look at Quality-Aware Runtime Assessment of Sensing Models in Multi-Device Environments
    Chulhong Min, Alessandro Montanari, Akhil Mathur, Fahim Kawsar
    17th ACM Conference on Embedded Networked Sensor Systems (SenSys 2019), November 2019.
    Publisher PDF
  • Resource Characterisation of Personal-scale Sensing Models on Edge Accelerators
    Mattia Antonini, Tran Huy Vu, Chulhong Min, Alessandro Montanari, Akhil Mathur, Fahim Kawsar
    First ACM International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (AIChallengeIoT 2019), November 2019.
    Publisher PDF
  • An Early Characterisation of Wearing Variability on Motion Signals for Wearables
    Chulhong Min, Akhil Mathur, Alessandro Montanari, Fahim Kawsar
    International Symposium on Wearable Computers (ISWC 2019), September 2019.
    Publisher PDF
  • Head Motion Tracking Through In-ear Wearables
    Andrea Ferlini, Alessandro Montanari, Cecilia Mascolo, Robert Harle
    1st International Workshop on Earable Computing (EarComp 2019), September 2019.
    Publisher PDF
  • Automatic Smile and Frown Recognition with Kinetic Earables
    Seungchul Lee, Chulhong Min, Alessandro Montanari, Akhil Mathur, Youngjae Chang, Junehwa Song, Fahim Kawsar
    10th Augmented Human International Conference (AH 2019), March 2019.
    Publisher PDF
  • Earables for Personal-Scale Behavior Analytics
    Fahim Kawsar, Chulhong Min, Akhil Mathur, Alessandro Montanari
    IEEE Pervasive Computing, October 2018.
    Publisher PDF
  • Surveying Areas in Developing Regions Through Context Aware Drone Mobility
    Alessandro Montanari, Fredrika Kringberg, Alice Valentini, Cecilia Mascolo, Amanda Prorok
    4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications (DroNet 2018), June 2018.
    Publisher PDF
  • Measuring Interaction Proxemics with Wearable Light Tags
    Alessandro Montanari, Zhao Tian, Elena Francu, Benjamin Lucas, Brian Jones, Xia Zhou, Cecilia Mascolo
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), March 2018.
    Publisher PDF
  • Detecting Emerging Activity-based Working Traits Through Wearable Technology
    Alessandro Montanari, Cecilia Mascolo, Kerstin Sailer, Sarfraz Nawaz
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), September 2017.
    Publisher PDF
  • A Study of Bluetooth Low Energy Performance for Human Proximity Detection in the Workplace [Best Paper Nominee]
    Alessandro Montanari, Sarfraz Nawaz, Cecilia Mascolo, Kerstin Sailer
    IEEE International Conference on Pervasive Computing and Communications (PerCom 2017), March 2017.
    Publisher PDF

See All Publications...

Patents

  • A System For The Detection Of Artifacts In Bio-Signal Sensors And The Synthesis Of Clean Data For Wearable Devices. {EP 21192515.1}
  • Model modification and deployment. {US 2021/0350280 A1}
  • Mobile Sensor And Nft Based Authentication. {323096-EP}
  • Sensor configuration based on other sensor context determination. {WO 20201/69739 A1}
  • Runtime assessment of sensors. {WO 2021/053444 A1}
  • Detection of facial expressions. {WO 2020/182447 A1}
  • Energy-aware processing system. {WO 2021/180664 A1}
  • Privacy-aware personal data store. {US 2016/0044039 A1}

Mentoring

I find it incredibly stimulating and inspiring mentoring and collaborating with self-motivated students. At Bell Labs we offer several on-site, paid internship positions. Click here for research internships and here for engineering internships. Typically, applications open in December for positions during June-September of next year. Feel free to reach out if you are interested in these opportunities.

Below are the students I have supervised and collaborated with:

Nokia Bell Labs Interns and Visitors

  • Abu Bakar (Northwestern University) — Logic-based Intelligence for Batteryless Sensors (2021)
  • Tousif Rahman (Newcastle University) — Logic-based Intelligence for Batteryless Sensors (2021)
  • Ananta Narayanan Balaji (NUS) — In-Ear Sensing (2021)
  • Hoang Truong (University of Colorado Boulder) — In-Ear Sensing (2021)
  • Shkurta Gashi (USI) — In-Ear Sensing (2021)
  • Manasa Kalanadhabhatta (University of Massachusetts Amherst) — FatigueSet: A Multi-modal Dataset for Modeling Mental Fatigue and Fatigability (2021)
  • Andrea Ferlini (University of Cambridge) — In-Ear PPG for Vital Signs (2020)
  • Manuja Sharma (University of Washington) — ePerceptive—Energy Reactive Embedded Intelligence for Batteryless Sensors (2019)
  • Dainius Jenkus (Newcastle University) — ePerceptive—Energy Reactive Embedded Intelligence for Batteryless Sensors (2019)
  • Mattia Antonini (FBK CREATE-NET and University of Trento) — Resource Characterisation of Personal-scale Sensing Models on Edge Accelerators (2019)
  • Dmitry Ermilov (Skoltech) — Design And Implementation Of Mobile Sensing Applications For Research In Behavioural Understanding (2018)
  • Seungchul Lee (KAIST) — Automatic Smile and Frown Recognition with Kinetic Earables (2018)

Thesis Supervision

  • Alexandru-Mihai Solot — Efficient Deep Learning For Small Object Detection On Embedded Systems
    2019 - University of Cambridge, Master of Philosophy in Advanced Computer Science
  • James Lowenthal — Graph-Based Search for Continuous Space with Application to Path-Planning for Drone-Based Area Surveying
    2018 - University of Cambridge, Computer Science Tripos – Part II
  • Fredrika Kringberg — A Path Planning Approach for Context Aware Autonomous UAVs used for Surveying Areas in Developing Regions
    2017 - KTH, Master of Science Thesis
  • Alice Valentini — Evaluation Of Deep Learning Techniques For Object Detection On Embedded Systems
    2017 - University of Bologna, Computer Science Master Thesis
  • Andrea Bucaletti — Design and Implementation of tuProlog User Interface Following the Java Scripting Engine Specification
    2015 - University of Bologna, Computer Engineering Master Thesis
  • Matteo Librenti — Input Management for tuProlog Graphical Interface in Java and Eclipse
    2013 - University of Bologna, Computer Engineering Bachelor Thesis
  • Alessio Mercurio — Class Loading in Android Applied to the tuProlog Interpreter
    2013 - University of Bologna, Computer Engineering Bachelor Thesis