ArticleWearable technologies2021
Versatile and non-versatile occupational back-support exoskeletons: A comparison in laboratory and field studies.
Article in Wearable technologies, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 34 citations in OpenAlex.
- Article
- A Review of Wearable Back-Support Exoskeletons for Preventing Work-Related Musculoskeletal Disorders.Biomimetics (Basel, Switzerland) · 2025Review
- Effects of a Wearable Assistive Device on Postural Control and Stability During Symmetric and Asymmetric Intermittent Trunk Flexion Tasks.Bioengineering (Basel, Switzerland) · 2025Article
- Preference-based assistance optimization for lifting and lowering with a soft back exosuit.Science advances · 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
- Active back exosuits demonstrate positive usability perceptions that drive intention-to-use in the field among logistic warehouse workers.Applied ergonomics · 2025Article
- A versatile knee exoskeleton mitigates quadriceps fatigue in lifting, lowering, and carrying tasks.Science robotics · 2024Article
- How Effective Are Forecasting Models in Predicting Effects of Exoskeletons on Fatigue Progression?Sensors (Basel, Switzerland) · 2024Article
- Dynamic and Static Assistive Strategies for a Tailored Occupational Back-Support Exoskeleton: Assessment on Real Tasks Carried Out by Railway Workers.Bioengineering (Basel, Switzerland) · 2024Article
- Online Ergonomic Evaluation in Realistic Manual Material Handling Task: Proof of Concept.Bioengineering (Basel, Switzerland) · 2023Article
- The effect of a movable headrest in shoulder assist device for overhead work.Wearable technologies · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assistive strategies for occupational back-support exoskeletons have focused, mostly, on lifting tasks. However, in occupational scenarios, it is important to account not only for lifting but also for other activities. This can be done exploiting human activity recognition algorithms that can identify which task the user is performing and trigger the appropriate assistive strategy. We refer to this ability as exoskeleton versatility. To evaluate versatility, we propose to focus both on the ability of the device to reduce muscle activation (efficacy) and on its interaction with the user (dynamic fit). To this end, we performed an experimental study involving healthy subjects replicating the working activities of a manufacturing plant. To compare versatile and non-versatile exoskeletons, our device, XoTrunk, was controlled with two different strategies. Correspondingly, we collected muscle activity, kinematic variables and users' subjective feedbacks. Also, we evaluated the task recognition performance of the device. The results show that XoTrunk is capable of reducing muscle activation by up to in lifting and in carrying. However, the non-versatile control strategy hindered the users' natural gait (e.g., reduction of hip flexion), which could potentially lower the exoskeleton acceptance. Detecting carrying activities and adapting the control strategy, resulted in a more natural gait (e.g., increase of hip flexion). The classifier analyzed in this work, showed promising performance (online accuracy > 91%). Finally, we conducted 9 hours of field testing, involving four users. Initial subjective feedbacks on the exoskeleton versatility, are presented at the end of this work.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.