Environment-Based Assistance Modulation for a Hip Exosuit via Computer Vision

Tricomi, Enrica; Mirko, Mossini,; Missiroli, Francesco; Lotti, Nicola; Zhang, Xiao-Hui; Xiloyannis, Michele; Roveda, Loris; Masia, Lorenzo

doi:10.1109/lra.2023.3256135

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…Instead, we relied on a comparison based on gross metabolic changes, utilizing a condition where the device was worn but turned off as a reference point. While this approach may be less realistic in real-world terms, it was chosen for practical experimental reasons and it is the most widely adopted in the literature 12,18,17,48 . This decision allowed us to capture the user's movement during the No Assistance condition for meaningful comparison and to isolate the active biomechanical effect of assistance from the passive effects of wearing the suits.…”

Section: Discussionmentioning

confidence: 99%

Soft robotic shorts improve outdoor walking efficiency in older adults

Tricomi,

Missiroli,

Xiloyannis

et al. 2024

Preprint

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As people grow older, their walking efficiency declines, posing constraints on mobility and affecting independence and overall life quality. While wearable assistive technologies are recognized as a potential solution for age-related movement challenges, few have proven effective for older adults, predominantly within controlled laboratory experiments. Here we present WalkON, a pair of soft robotic shorts designed to enhance walking efficiency for older individuals by assisting hip flexion. To assess the impact of WalkON in daily walking activities, we initially conducted a technology assessment with young adults on a demanding outdoor uphill 500m hiking trail. Subsequently, we validated our findings with a group of older adults walking on a flat outdoor 400m track. WalkON significantly reduced the metabolic cost of transport by 17% for young adults during uphill walking. Concurrently, participants reported high perceived control over their voluntary movements (self-reported mean score of 5.67 out of 7 on a Likert scale). Similarly, older adults experienced a 9% reduction in metabolic cost when using WalkON during level ground walking, while retaining a strong sense of movement control (mean score of 5.85 out of 7). These findings emphasize the potential of wearable robotic assistive devices to enhance energy efficiency in daily outdoor walking, suggesting promising implications for promoting physical well-being and advancing mobility, particularly during the later stages of life.

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Section: Discussionmentioning

confidence: 99%

Soft robotic shorts improve outdoor walking efficiency in older adults

Tricomi,

Missiroli,

Xiloyannis

et al. 2024

Preprint

Self Cite

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“…Unlike many previous studies [12]- [18], we uniquely studied sequential image classification, which takes advantage of the cyclical nature of walking and uses temporal relations in the observed environment. This contribution allowed us to exceed the previous state-of-the-art [12], whereby our 3D-CNN model (98.3% accuracy) outperformed the original StairNet model (97.2% accuracy) when evaluated on the same dataset using our video-based validation method.…”

Section: Mobilenetv2 + Lstm (Sequence-to-one Training; Sequence-to-on...mentioning

confidence: 99%

Sequential Image Classification of Human-Robot Walking Environments using Temporal Neural Networks

Ivanyuk-Skulskiy,

Kurbis,

Mihailidis

et al. 2023

Preprint

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Robotic prosthetic legs and exoskeletons require real-time and accurate estimation of the walking environment for smooth transitions between different locomotion mode controllers. However, previous studies have mainly been limited to static image classification, therein ignoring the temporal dynamics of human-robot locomotion. Motivated by these limitations, here we developed several state-of-the-art temporal convolutional neural networks (CNNs) to compare the performances between static vs. sequential image classification of real-world walking environments (i.e., level-ground terrain, incline stairs, and transitions to and from stairs). Using our large-scale image dataset, we trained a number of encoder networks such as VGG, MobileNetV2, ViT, and MobileViT, each coupled with a temporal long short-term memory (LSTM) backbone. We also trained MoViNet, a new video classification model designed for mobile and embedded devices, to further compare the performances between 2D and 3D temporal deep learning models. Our 3D network outperformed all the hybrid 2D encoders with LSTM backbones and the 2D CNN baseline model in terms of classification accuracy, suggesting that network architecture can play an important role in performance. However, although our 3D neural network achieved the highest classification accuracy, it had disproportionally higher computational and memory storage requirements, which can be disadvantageous for real-time control of robotic leg prostheses and exoskeletons with limited onboard resources.

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“…Mihailidis is with the Institute of Biomedical Engineering and the Robotics Institute, University of Toronto, Toronto, Canada, and the KITE Re-walking terrains, including level-ground, stairs, and other obstacles, though lacked onboard inferencing. Some researchers have used classical machine learning methods with success [12]- [16]. While both deep learning and non-deep learning models have shown good accuracy performance, they have not focused on deployment and efficiency as the main objectives.…”

Section: Introductionmentioning

confidence: 99%

AI-Powered Smart Glasses for Sensing and Recognition of Human-Robot Walking Environments

Rossos,

Mihailidis,

Laschowski

2023

Preprint

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Environment sensing and recognition can allow humans and robots to dynamically adapt to different walking terrains. However, fast and accurate visual perception is challenging, especially on embedded devices with limited computational resources. The purpose of this study was to develop a novel pair of AI-powered smart glasses for onboard sensing and recognition of human-robot walking environments with high accuracy and low latency. We used a Raspberry Pi Pico microcontroller and an ArduCam HM0360 low-power camera, both of which interface with the eyeglass frames using 3D-printed mounts that we custom-designed. We trained and optimized a lightweight and efficient convolutional neural network using a MobileNetV1 backbone to classify the walking terrain as either indoor surfaces, outdoor surfaces (grass and dirt), or outdoor surfaces (paved) using over 62,500 egocentric images that we adapted and manually labelled from the Meta Ego4D dataset. We then compiled and deployed our deep learning model using TensorFlow Lite Micro and post-training quantization to create a minimized byte array model of size 0.31MB. Our system was able to accurately predict complex walking environments with 93.6% classification accuracy and had an embedded inference speed of 1.5 seconds during online experiments using the integrated camera and microcontroller. Our AI-powered smart glasses open new opportunities for visual perception of human-robot walking environments where embedded inference and a low form factor is required. Future research will focus on improving the onboard inference speed and miniaturization of the mechatronic components.

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Environment-Based Assistance Modulation for a Hip Exosuit via Computer Vision

Cited by 17 publications

References 22 publications

Soft robotic shorts improve outdoor walking efficiency in older adults

Soft robotic shorts improve outdoor walking efficiency in older adults

Sequential Image Classification of Human-Robot Walking Environments using Temporal Neural Networks

AI-Powered Smart Glasses for Sensing and Recognition of Human-Robot Walking Environments

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