The University of Southampton
University of Southampton Institutional Repository

Real-time room occupancy estimation with bayesian machine learning using a single PIR sensor and microcontroller

Leech, Charles, Raykov, Yordan P., Ozer, Emre and Merrett, Geoff V. (2017) Real-time room occupancy estimation with bayesian machine learning using a single PIR sensor and microcontroller At IEEE Sensors Applications Symposium (SAS) 2017, Glassboro, United States. 13 - 15 Mar 2017. 6 pp.

Record type: Conference or Workshop Item (Paper)


This paper presents the implementation and deployment of a compute/memory intensive non-parametric Bayesian machine learning algorithm on a microcontroller unit (MCU) to estimate room occupancy in a Smart Room using a single analogue PIR sensor. We envisage an IoT device consisting of a resource-constrained MCU, PIR sensor and a battery running the occupancy estimation algorithm and operating over days or months without recharging or replacing the battery. Both hardware-independent and hardware-dependent optimizations are performed to reduce memory footprint and yet provide acceptable real-time performance while consuming less energy. We show a significant reduction in the on-chip memory usage in the MCUs by the algorithm through optimisation of the machine learning models and of the static memory footprint and dynamic memory usage. We also show that a low-end MCU does not meet the real-time requirements of the application without causing high average power consumption. However, a moderately high-performance MCU with a higher clock frequency and hardware floating-point unit provides 19x improvement in the execution time of the algorithm, better meeting the real-time specification of the application and reducing power consumption. Further, we estimate the battery lifetime of the IoT device if it operates continuously in a Smart Room. With a typical size battery, an IoT device consisting of a Cortex-M4F MCU and PIR sensor can operate for more than a month without replacement or recharging of the battery while running the compute-intensive Bayesian machine learning algorithm.

PDF bare_conf.pdf - Accepted Manuscript
Download (967kB)

More information

Accepted/In Press date: 28 January 2017
Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Venue - Dates: IEEE Sensors Applications Symposium (SAS) 2017, Glassboro, United States, 2017-03-13 - 2017-03-15
Organisations: Electronics & Computer Science


Local EPrints ID: 405697
PURE UUID: 1a9a7bd1-4319-4894-8b90-d9cb2d8dfab2
ORCID for Charles Leech: ORCID iD
ORCID for Geoff V. Merrett: ORCID iD

Catalogue record

Date deposited: 18 Feb 2017 00:22
Last modified: 18 Jul 2017 04:05

Export record


Author: Charles Leech ORCID iD
Author: Yordan P. Raykov
Author: Emre Ozer
Author: Geoff V. Merrett ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.