ArticleScientific reports2023
IMU-based human activity recognition and payload classification for low-back exoskeletons.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Intention Prediction for Active Upper-Limb Exoskeletons in Industrial Applications: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Optimizing Sensor and Data Selection on Lower Limbs via Deep Learning for Real-Time Human Activity Recognition.IEEE transactions on human-machine systems · 2026Article
- Fully textile passive wireless sensing for human movement monitoring with multiple sensors.Frontiers in bioengineering and biotechnology · 2026Article
- Wearable sensor technologies for individuals with back pain: a scoping review.Pain management · 2025Article
- Human Activity Recognition via Attention-Augmented TCN-BiGRU Fusion.Sensors (Basel, Switzerland) · 2025Article
- Computer vision and tactile glove: A multimodal model in lifting task risk assessment.Applied ergonomics · 2025Article
- Upper limb human-exoskeleton system motion state classification based on semg: application of CNN-BiLSTM-attention model.Scientific reports · 2025Article
- Human activity recognition algorithms for manual material handling activities.Scientific reports · 2025Article
- The perceptual and biomechanical effects of scaling back exosuit assistance to changing task demands.Scientific reports · 2025Article
- Low Back Exoskeletons in Industry 5.0: From Machines to Perceiving Co-Pilots-A State-of-the-Art Review.Sensors (Basel, Switzerland) · 2025Review
- Article
- A Recurrent Deep Network for Gait Phase Identification from EMG Signals During Exoskeleton-Assisted Walking.Sensors (Basel, Switzerland) · 2024Article
- Exploring the Possibility of Photoplethysmography-Based Human Activity Recognition Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Disturbance rejection model predictive control of lower limb rehabilitation exoskeleton.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Nowadays, work-related musculoskeletal disorders have a drastic impact on a large part of the world population. In particular, low-back pain counts as the leading cause of absence from work in the industrial sector. Robotic exoskeletons have great potential to improve industrial workers' health and life quality. Nonetheless, current solutions are often limited by sub-optimal control systems. Due to the dynamic environment in which they are used, failure to adapt to the wearer and the task may be limiting exoskeleton adoption in occupational scenarios. In this scope, we present a deep-learning-based approach exploiting inertial sensors to provide industrial exoskeletons with human activity recognition and adaptive payload compensation. Inertial measurement units are easily wearable or embeddable in any industrial exoskeleton. We exploited Long-Short Term Memory networks both to perform human activity recognition and to classify the weight of lifted objects up to 15 kg. We found a median F1 score of [Formula: see text] (activity recognition) and [Formula: see text] (payload estimation) with subject-specific models trained and tested on 12 (6M-6F) young healthy volunteers. We also succeeded in evaluating the applicability of this approach with an in-lab real-time test in a simulated target scenario. These high-level algorithms may be useful to fully exploit the potential of powered exoskeletons to achieve symbiotic human-robot interaction.
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