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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

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Related Experiment Video

Updated: Jun 11, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Efficient uncertainty aware human activity recognition on microcontrollers using hyperdimensional computing and

Ismail Lamaakal1, Chaymae Yahyati2, Yassine Maleh3

  • 1Multidisciplinary Faculty of Nador, Mohammed Premier University, Oujda, Morocco. ismail.lamaakal@ieee.org.

Scientific Reports
|June 9, 2026
PubMed
Summary
This summary is machine-generated.

We developed a new method for wearable human activity recognition (HAR) that provides reliable uncertainty on microcontrollers. This approach offers efficient and trustworthy predictions for daily activities, even with sensor shifts.

Related Experiment Videos

Last Updated: Jun 11, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

  • Computer Science
  • Machine Learning
  • Wearable Technology

Background:

  • Wearable human activity recognition (HAR) faces challenges in providing reliable uncertainty on resource-constrained microcontrollers.
  • Existing embedded HAR methods struggle with sensor shifts and exceed computational budgets.

Purpose of the Study:

  • To propose a novel on-device framework for human activity recognition that delivers reliable, interpretable uncertainty on tiny microcontrollers.
  • To address the limitations of existing embedded HAR approaches regarding computational cost and robustness to sensor variations.

Main Methods:

  • Developed hyperdimensional distance- and uncertainty-aware human activity recognition (HDUQ-HAR), an on-device hyperdimensional computing (HDC) framework.
  • Encoded inertial measurement unit (IMU) windows into hypervectors, classified via prototype similarity, and derived lightweight uncertainty signals.
  • Integrated a label-conditional conformal layer for set-valued predictions with coverage guarantees and human-readable reason codes.

Main Results:

  • HDUQ-HAR achieved near-target coverage and near-singleton sets on diverse datasets with subject-out splits and realistic sensor shifts.
  • Demonstrated robust shift and out-of-distribution detection with high AUROC scores (0.92-0.96).
  • Operated efficiently on Cortex-M4 microcontrollers within strict latency, RAM, and Flash constraints (3-5 ms/window, ~6-9 KB RAM, ~5-7 KB Flash).

Conclusions:

  • The proposed HDUQ-HAR method successfully unifies HDC geometry with label-conditional conformal prediction for efficient and reliable wearable HAR.
  • Demonstrated that lightweight, calibrated set-valued predictions with actionable explanations are achievable on embedded systems.
  • The framework expands gracefully under sensor shifts and provides trustworthy insights for practitioners.