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📅 2026 ICRA 2026

A Modular, Wireless and Wearable Biosignal Acquisition Platform

Authors: Mohamad Reza Shahabian Alashti, Co-authors TBD
Published In ICRA 2026
Year 2026
Abstract
Presenting a modular and wireless platform for acquiring biosignals from wearable sensors, enabling large-scale data collection for machine learning applications in healthcare and assistive robotics.

Paper under review for the International Conference on Robotics and Automation (ICRA) 2026.

This work presents a comprehensive platform for wireless biosignal acquisition, designed to support research in wearable robotics and human-machine interaction.

📅 2025 ICSR 2025

Towards Memory-Driven Agentic AI for Human Activity Recognition

Authors: Mohamad Reza Shahabian Alashti, Co-authors
Published In ICSR 2025
Year 2025
Abstract
Exploring memory-driven agentic AI architectures for human activity recognition, enabling adaptive and goal-oriented behavior in ambient assisted living scenarios.

Accepted for publication at the International Conference on Social Robotics (ICSR) 2025.

This paper introduces novel concepts from agentic AI to human activity recognition, proposing memory-driven systems that can adapt their behavior based on context and user history.

📅 2024 BioRob 2024

Efficient Skeleton-based Human Activity Recognition in Ambient Assisted Living Scenarios with Multi-view CNN

Authors: Mohamad Reza Shahabian Alashti, Farshid Amirabdollahian, Co-authors
Published In BioRob 2024
Year 2024
Abstract
Presenting an efficient CNN-based approach for skeleton-based human activity recognition using multi-view camera systems, optimized for real-time performance in assisted living environments.

Published at the International Conference on Biomedical Robotics and Biomechatronics (BioRob) 2024.

This work demonstrates how multi-view skeleton data can be effectively processed using convolutional neural networks for real-time activity recognition in smart home environments.

📅 2024 BioRob 2024

Robotic Vision and Multi-View Synergy: Action and Activity Recognition in Assisted Living Scenarios

Authors: Mohamad Reza Shahabian Alashti, Farshid Amirabdollahian, Co-authors
Published In BioRob 2024
Year 2024
Abstract
Investigating the synergy between robotic vision systems and multi-view camera networks for robust action and activity recognition in real-world assisted living environments.

Published at the International Conference on Biomedical Robotics and Biomechatronics (BioRob) 2024.

This paper explores how multiple camera viewpoints can be integrated with robotic perception systems to achieve more reliable activity recognition for elderly care applications.

📅 2023 Conference/Journal TBD

Lightweight Human Activity Recognition for Ambient Assisted Living

Authors: Mohamad Reza Shahabian Alashti, Farshid Amirabdollahian
Published In Conference/Journal TBD
Year 2023
Abstract
Developing lightweight deep learning models for human activity recognition that can run efficiently on edge devices while maintaining high accuracy for assisted living applications.

This work focuses on model compression and optimization techniques to enable real-time HAR on resource-constrained devices commonly found in smart home environments.

Key contributions include:

  • Efficient network architectures for edge deployment
  • Knowledge distillation for model compression
  • Real-time performance on embedded devices
  • Maintained accuracy with reduced computational requirements
📅 2023 Dataset Release

RHM: Robot House Multi-view Human Activity Recognition Dataset

Authors: Mohamad Reza Shahabian Alashti, Farshid Amirabdollahian, Co-authors
Published In Dataset Release
Year 2023
Abstract
Presenting RHM-HAR, a large-scale RGB multi-view human activity recognition dataset collected in the Robot House at University of Hertfordshire, featuring natural activities in a realistic smart home environment.

A comprehensive RGB video dataset including:

  • Multiple camera viewpoints covering entire living spaces
  • Natural activities performed in realistic home settings
  • Long-duration recordings capturing activity variations
  • Comprehensive annotations and metadata
  • Suitable for various computer vision tasks

The Robot House provides a unique realistic testbed for ambient assisted living research, and this dataset captures genuine human behaviors in that environment.

📅 2023 Dataset Release

RHM-HAR-SK: A multi-view dataset with skeleton data for Ambient Assisted Living Research

Authors: Mohamad Reza Shahabian Alashti, Farshid Amirabdollahian, Co-authors
Published In Dataset Release
Year 2023
Abstract
Introducing RHM-HAR-SK, a comprehensive multi-view skeleton-based human activity recognition dataset designed for ambient assisted living research with multiple synchronized camera viewpoints.

A publicly available dataset featuring:

  • Multi-view synchronized skeleton sequences
  • Diverse activities relevant to elderly care
  • Multiple subjects with varied demographics
  • High-quality 3D pose annotations
  • Benchmark evaluation protocols

The dataset has been used by multiple research groups for developing and evaluating HAR algorithms in assisted living contexts.

📅 2022 Alan Turing Institute Report

Data augmentation and synthetic data generation for low-frequency and sparse data problems

Authors: Mohamad Reza Shahabian Alashti, Alan Turing Institute Team, AMRC Collaborators
Published In Alan Turing Institute Report
Year 2022
Abstract
Presenting methods for data augmentation and synthetic data generation to address challenges in low-frequency and sparse datasets common in industrial manufacturing and other domains.
View Paper

Technical report from the Data Study Group collaboration with the Alan Turing Institute and Advanced Manufacturing Research Centre.

Key contributions:

  • Novel augmentation strategies for sparse datasets
  • Synthetic data generation preserving statistical properties
  • Validation approaches for augmented data
  • Application to real-world manufacturing challenges
  • Guidelines for practitioners working with limited data
📅 2021 Workshop/Competition

Human Activity Recognition at Home: Benchmarks and Competition

Authors: Mohamad Reza Shahabian Alashti, Farshid Amirabdollahian, Co-authors
Published In Workshop/Competition
Year 2021
Abstract
Organizing and presenting benchmark results from a human activity recognition competition focused on home environments, establishing evaluation protocols for the research community.

This work established standardized benchmarks and evaluation protocols for HAR in home environments, facilitating fair comparison across different approaches and promoting reproducible research.

📅 2021 Conference/Journal TBD

Affordable Robot Mapping using Omnidirectional Vision

Authors: Mohamad Reza Shahabian Alashti, Co-authors
Published In Conference/Journal TBD
Year 2021
Abstract
Presenting a cost-effective approach to robot mapping and localization using omnidirectional camera systems, making SLAM more accessible for research and educational purposes.

This work demonstrates how affordable omnidirectional cameras can be used for simultaneous localization and mapping (SLAM), reducing the cost barrier for robotics research and education.

📅 2018 Conference/Journal

Automatic ROI Detection in Lumbar Spine MRI

Authors: Mohamad Reza Shahabian Alashti, Co-authors
Published In Conference/Journal
Year 2018
Abstract
Developing automated methods for detecting regions of interest in lumbar spine MRI images, supporting medical diagnosis and treatment planning for spinal conditions.

This work from the MSc research applies machine learning and image processing techniques to automatically identify relevant anatomical structures in medical imaging, assisting radiologists in diagnosis and treatment planning.

📅 2017 Conference/Technical Report

FARAT1: An Upper Body Exoskeleton Robot

Authors: Mohamad Reza Shahabian Alashti, SYNTECH Team
Published In Conference/Technical Report
Year 2017
Abstract
Presenting the design and development of FARAT1, an upper body exoskeleton robot for rehabilitation and assistive applications, featuring novel mechanical and control solutions.

Part of the work at SYNTECH Technology & Innovation Center, this project developed a wearable exoskeleton for upper body assistance and rehabilitation, incorporating advanced mechatronics and control systems.

📅 2017 Conference/Technical Report

Mechanical Basic and Detailed Design for the Redundant Arm SAAM applied on a Domestic Service Robot

Authors: Mohamad Reza Shahabian Alashti, SYNTECH Team
Published In Conference/Technical Report
Year 2017
Abstract
Detailing the mechanical design and development of SAAM, a 7-DoF redundant robotic arm for domestic service robots, with focus on kinematics, workspace analysis, and manufacturing considerations.

Comprehensive design work for the SAAM (7-DoF) robotic arm used in domestic service robots, covering:

  • Kinematic analysis and workspace optimization
  • Mechanical component selection and design
  • Manufacturing and assembly procedures
  • Integration with mobile robot platforms
  • Control system architecture

This arm was successfully deployed on service robots competing in RoboCup @Home league.