Publications by authors named "Lorenzo Grazi"

Humans naturally employ muscle coactivation to facilitate a broad range of movements, enhancing joint stability and movement accuracy. However, excessive muscle coactivation can become unfavorable or even detrimental. This phenomenon is often observed in industrial workers who endure repetitive or prolonged joint stress, particularly in areas such as the shoulders.

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Upper-limb occupational exoskeletons to support the workers' upper arms are typically designed to provide antigravitational support. Although typical work activities require workers to perform static and dynamic actions, the majority of the studies in literature investigated the effects of upper-limb occupational exoskeletons in static and quasi-static activities, while only a few works focused on dynamic tasks. This article presents a systematic evaluation of the effects of different levels of antigravitational support (from about 60% to 100% of the arm gravitational load) provided by a passive upper-limb occupational exoskeleton on muscles' activity during repetitive arm movements.

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The large-scale adoption of occupational exoskeletons (OEs) will only happen if clear evidence of effectiveness of the devices is available. Performing product-specific field validation studies would allow the stakeholders and decision-makers (e.g.

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This article presents the H-PULSE, a novel semi-passive upper-limb exoskeleton for worker assistance, with motorized tuning of the assistive level. The H-PULSE presents novel design features compared to other passive industrial exoskeletons for the upper limbs, namely joint angle sensors for measuring shoulder flexion/extension and a novel active mechanism for regulating the assistance level. These features could enhance the effectiveness of the system.

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Article Synopsis
  • Exoskeletons for assisting people with disabilities need smart algorithms and human-robot interfaces to detect users' movement intentions using surface electromyographic (sEMG) signals, though high variability among individuals poses challenges for real-time applications.
  • A machine-learning algorithm was developed to detect movement intentions from sEMG signals by analyzing data from healthy participants performing reaching tasks with an upper-limb exoskeleton.
  • The algorithm achieved a maximum sensitivity of 89.3% in identifying movement forwards and 60.9% for backward movements, showing promise in enhancing assistive technology for those with severe arm disabilities.*
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The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize and classify the users' movement when performing various lifting tasks. To this end, the movement-decoding algorithm should work robustly with different users and recognize different lifting techniques.

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Repetitive lifting of heavy loads increases the risk of back pain and even lumbar vertebral injuries to workers. Active exoskeletons can help workers lift loads by providing power assistance, and therefore reduce the moment and force applied on L5/S1 joint of human body when performing lifting tasks. However, most existing active exoskeletons for lifting assistance are unable to automatically detect user's lift movement, which limits the wide application of active exoskeletons in factories.

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We present a novel assistive control strategy for a robotic hip exoskeleton for assisting hip flexion/extension, based on a proportional Electromyography (EMG) strategy. The novelty of the proposed controller relies on the use of the Gastrocnemius Medialis (GM) EMG signal instead of a hip flexor muscle, to control the hip flexion torque. This strategy has two main advantages: first, avoiding the placement of the EMG electrodes at the human-robot interface can reduce discomfort issues for the user and motion artifacts of the recorded signals; second, using a powerful signal for control, such as the GM, could improve the reliability of the control system.

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In this paper we present a novel EMG-based assistive control strategy for lower-limb exoskeletons. An active pelvis orthosis (APO) generates torque profiles for the hip flexion motion assistance, according to the Gastrocnemius Medialis EMG signal. The strategy has been tested on one healthy subject: experimental results show that the user is able to reduce his muscular activation when the assistance is switched on with respect to the free walking condition.

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