Master 2 Research Internship – Human Activity Recognition, Dataset
Synthesis and Analysis Methods
Job Description
IMT Atlantique and IMT Nord-Europe are leading Technological University
within IMT (Institut Mines-Télécom)—France’s largest group of
engineering schools. Their mission is to support societal transitions,
train responsible engineers, and harness scientific and technical
excellence for education, research, and innovation.
The position is based in Brest, within the RAMBO team (Robot
interaction, Ambient system, Machine learning, Behavior, Optimization –
IMT Atlantique) at the Lab-STICC laboratory (UMR CNRS 6285). This
research project is conducted in collaboration with the HIDE team (Human
Interaction Data Ecosystem – IMT Nord Europe) at CERI SN. The teams
share a common interest in analyzing interactions (between humans and
systems), with a particular focus on applications involving assistance
for vulnerable individuals. The ObsAVQ project aims to develop methods
for generating reports on Activities of Daily Living (ADL) performed at
home over extended observation periods.
Scientific Context and Internship Objectives
In the context of monitoring health status changes at home, the focus is
on generating reports regarding Activities of Daily Living (ADL) [1]
observed in the home environment, intended to prepare for
pre-consultation discussions with a primary care physician. The project
assumes the availability of a list of events—characterized by semantic
annotations (recognized activity type) and temporal data
(timestamps)—identified by sensors integrated into the home; the
objective is to identify trends over long durations, ranging from one
week to several years.
More specifically, within the wide range of ADLs, the work focuses on:
(1) sleep, (2) nutrition, and (3) social interactions. Data regarding
these activities will be derived from an acoustic analysis of the home
environment.
Algorithms for analyzing Activities of Daily Living (ADLs) require
labeled training data (ground-truth datasets). Compiling a dataset using
real users over the long term is costly and time-consuming.
Consequently, this study focuses on dataset “synthesis” techniques [2]:
the approach involves first developing a behavioral model, then running
it over the required simulation period while varying parameters to
challenge the learning and classification algorithms.
Regarding the methodology for dataset construction, we will use medical
descriptions of symptoms observed during health anomalies—specifically
their type, frequency, and time of occurrence. The goal of the machine
learning process will be to identify the anomalies that caused the
symptoms exhibited by the individual.
Objectives:
* O1: Generation of a home-based ADL activity log dataset derived from
behavioral models, with ground truth known by design.
* O2: Development of an offline method for automatically generating
activity reports using machine learning, trained on the dataset
generated in O1.
* O3: Evaluation of the proposed method using the dataset generated in
O1, and comparison with the state of the art.
References:
1. Edemekong, Peter F., Deb Bomgaars, Sukesh Sukumaran, and Shoshana B.
Levy. “Activities of daily living.” (2019).
2. Weihang You, Hanqi Jiang, Jiahui Li, Zishuai Liu, Tianming Liu, Jin
Lu, and Fei Dou. 2026. ADLGen: Synthesizing Symbolic,
Event-Triggered Sensor Sequences for Smart-Home Human Activity
Modeling. In Proceedings of the 2026 ACM/IEEE International
Conference on Embedded Artificial Intelligence and Sensing Systems
(SenSys’26). Association for Computing Machinery, New York, NY, USA,
1029–1042.
Position Requirements
Skills and Knowledge:
* Candidates must be currently pursuing a Master 2 degree in Computer
Science (5 years of higher education or equivalent).
* Programming and machine learning skills are highly desirable
(demonstrated via internships, reports, publications, code samples,
etc.).
* Initial research experience or basic training.
* Proficiency in French and English (reading, writing, and speaking).
Conditions
* The internship is scheduled for a duration of 6 months, starting in
January/February 2027.
* Application deadline: 01/12/2026.
* An interview lasting approximately 30 minutes (video conferencing
available) to discuss your background, motivation, skills, and
career goals.
* Stipend: 4.50€/h (i.e about 580-640€/month)
* Location: Plouzané (29), France – i.e. the Brest campus of IMT
Atlantique
Application Package Requirements
* Detailed CV.
* Cover letter explaining your interest in the topic and your relevant
skills.
* Academic transcripts for M1 and M2 (or equivalent).
Contacts
* Christophe LOHR, Associate Professor,
christophe.lohr[at]imt-atlantique.fr
* Mihai ANDRIES, Associate Professor, mihai.andries[at]imt-atlantique.fr
* Anthony FLEURY, Full Professor, anthony.fleury[at]imt-nord-europe.fr
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