ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies
July 7, 2022
In this paper, we introduce TinyOdom, a framework for training and deploying neural inertial models on URC hardware.
ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
July 7, 2022
earable, network architecture search, neural networks, machine learning, datasets, filters, human activity, head-pose, TinyML, optimization, hardware-in-the-loop
tinyML Summit
March 2022
neural networks, edge computing, IoT platforms, AI-based inference, TinyML, activity detection models
IEEE Transactions on Signal Processing
March 3, 2022
Bayesian estimation methods for sparse blind deconvolution problems conventionally employ Bernoulli-Gaussian (BG) prior for modeling sparse sequences and utilize Markov Chain Monte Carlo (MCMC) methods for the estimation of unknowns. However, the discrete nature of the BG model creates computational bottlenecks, preventing efficient exploration of the probability space even with the recently proposed enhanced sampler schemes. To address this issue, we propose an alternative MCMC method by modeling the sparse sequences using the Normal-Inverse-Gamma (NIG) prior. We derive effective Gibbs samplers for this prior and illustrate that the computational burden associated with the BG model can be eliminated by transferring the problem into a completely continuous-valued framework. In addition to sparsity, we also incorporate time and frequency domain constraints on the convolving sequences. We demonstrate the effectiveness of the proposed methods via extensive simulations and characterize computational gains relative to the existing methods that utilize BG modeling.
IEEE Sensors Journal
November 15, 2022
feature projection, Internet of Things, machine learning, microcontrollers, model compression, neural architecture search, neural networks, optimization, sensors, TinyML
This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. We characterize a closed-loopwidely applicable workflow of ML model development for microcontroller-class devices and show that several classes of applications adopt a specific instance of it.
International Conference on Learning Representations (ICLR)
November 13, 2021
privacy, user re-identification, wrist-worn accelerometers
We characterize the re-identification risks inherent in motion sensor data collected from wrist-worn devices in users’ natural environment. We use an open-set formulation, train a deep learning architecture with a new loss function, and apply our model to a new data set.
IEEE/ACM Symposium on Edge Computing (SEC)
December 14, 2021
software security engineering, mobile platform security
Application latency requirements, privacy, and security concerns have naturally pushed computing onto smartphone and IoT devices in a decentralized manner. In response to these demands, researchers have developed micro-runtimes for WebAssembly (Wasm) on IoT devices to enable streaming applications to a runtime that can run the target binaries that are independent of the device. However, the migration of Wasm and the associated security research has neglected the urgent needs of access control on bare-metal, memory management unit (MMU)-less IoT devices that are sensing and actuating upon the physical environment. This paper presents Aerogel, an access control framework that addresses security gaps between the bare-metal IoT devices and the Wasm execution environment concerning access control for sensors, actuators, processor energy usage, and memory usage. In particular, we treat the runtime as a multi-tenant environment, where each Wasm-based application is a tenant. We leverage the inherent sandboxing mechanisms of Wasm to enforce the access control policies to sensors and actuators without trusting the bare-metal operating system. We evaluate our approach on a representative IoT development board: a cortexM4 based development board (nRF52840). Our results show that Aerogel can effectively enforce compute resource and peripheral access control policies while introducing as little as 0.19% to 1.04% runtime overhead and consuming only 18.8% to 45.9% extra energy.
This paper presents Aerogel, an access control framework that addresses security gaps between the bare-metal IoT devices and the Wasm execution environment concerning access control for sensors, actuators, processor energy usage, and memory usage.
ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies
July 7, 2022
C++
C
Jupyter Notebook
Python
ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
July 7, 2022
C++
C
Python
MATLAB
Jupyter Notebook
https://github.com/ARM-software/mango/tree/main/examples/THIN-Bayes
tinyML Summit
March 2022
Python
IEEE Transactions on Signal Processing
March 3, 2022
MATLAB
International Conference on Learning Representations (ICLR)
November 13, 2021
Jupyter Notebook
The Auritus dataset has 2.43 million inertial samples related to head and full-body movements, consisting of 34 head poses and 9 activities from 45 volunteers. There are three main folders in the dataset, namely Activity Detection, Simple Head Pose and Complex Head Pose. It also contains physiological information about participants in the file Participant_Info.csv, target information for simple and complex head pose in the file HP_target_info.csv, and the IRB approval in the file IRB_Approval.pdf. Simple head pose corresponds to head movements from a origin marker to a target marker and back. Complex head pose corresponds to head movements from a origin marker to target marker A, target marker A to target marker B, and target marker B to origin.
ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
July 7, 2022
TR&D3 will make it possible for temporally-precise mHealth interventions for maintaining health and managing the growing burden of chronic diseases, to be realized on participants’ personal devices with minimal impact on battery life so as not to interfere with routine usage of these devices.
