Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation

Gonçalves, Cláudio Ubiratan; Lopes, João; Moccia, Sara; Berardini, Daniele; Migliorelli, Lucia; Santos, Cristina P.

doi:10.1016/j.eswa.2023.120288

Cited by 4 publications

(4 citation statements)

References 42 publications

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“…There are some recent publications on vision systems for human motion/intent recognition. For example, Goncalves et al developed a deeplearning-based approach for the recognition of human motion (such as walking, stopping, and left/right turn) with an accuracy of 93%, but this method utilizes a walker-mounted RGB-D camera to obtain lower-body motion video, and thus cannot be used for wearable robot control [41]. Darafsh et al developed a vision system that utilizes videos from stationary-mounted cameras to distinguish people who intend to pass through an auto-matic door from those who do not [42].…”

Section: Discussionmentioning

confidence: 99%

Vision-Based Recognition of Human Motion Intent during Staircase Approaching

Islam

Haque

Imtiaz

et al. 2023

Sensors

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Walking in real-world environments involves constant decision-making, e.g., when approaching a staircase, an individual decides whether to engage (climbing the stairs) or avoid. For the control of assistive robots (e.g., robotic lower-limb prostheses), recognizing such motion intent is an important but challenging task, primarily due to the lack of available information. This paper presents a novel vision-based method to recognize an individual’s motion intent when approaching a staircase before the potential transition of motion mode (walking to stair climbing) occurs. Leveraging the egocentric images from a head-mounted camera, the authors trained a YOLOv5 object detection model to detect staircases. Subsequently, an AdaBoost and gradient boost (GB) classifier was developed to recognize the individual’s intention of engaging or avoiding the upcoming stairway. This novel method has been demonstrated to provide reliable (97.69%) recognition at least 2 steps before the potential mode transition, which is expected to provide ample time for the controller mode transition in an assistive robot in real-world use.

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Section: Discussionmentioning

confidence: 99%

Vision-Based Recognition of Human Motion Intent during Staircase Approaching

Islam

Haque

Imtiaz

et al. 2023

Sensors

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“…In other words, the system acquires a window of data and its pre-processing and classification should be performed before the end of acquisition of the following time window. The computational complexity of the algorithms and the computing units included in the system play a critical role in the real-time compliance ( Avram and Pop, 2023 ; de la Cal et al, 2023 ; Gonçalves et al, 2023 ; Saliba et al, 2023 ; Zeng et al, 2023 ). Delayed or sluggish processing can hinder the effectiveness of AAL systems in providing timely assistance, which is crucial for ensuring the safety and well-being of individuals.…”

Section: Har Processing Chainmentioning

confidence: 99%

Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

Guerra,

Torti,

Marenzi

et al. 2023

Front. Neurosci.

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Ambient Assisted Living is a concept that focuses on using technology to support and enhance the quality of life and well-being of frail or elderly individuals in both indoor and outdoor environments. It aims at empowering individuals to maintain their independence and autonomy while ensuring their safety and providing assistance when needed. Human Activity Recognition is widely regarded as the most popular methodology within the field of Ambient Assisted Living. Human Activity Recognition involves automatically detecting and classifying the activities performed by individuals using sensor-based systems. Researchers have employed various methodologies, utilizing wearable and/or non-wearable sensors, and employing algorithms ranging from simple threshold-based techniques to more advanced deep learning approaches. In this review, literature from the past decade is critically examined, specifically exploring the technological aspects of Human Activity Recognition in Ambient Assisted Living. An exhaustive analysis of the methodologies adopted, highlighting their strengths and weaknesses is provided. Finally, challenges encountered in the field of Human Activity Recognition for Ambient Assisted Living are thoroughly discussed. These challenges encompass issues related to data collection, model training, real-time performance, generalizability, and user acceptance. Miniaturization, unobtrusiveness, energy harvesting and communication efficiency will be the crucial factors for new wearable solutions.

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“…It is challenging due to many actions, varying camera angles, similarities between actions, and changes in environmental conditions. It has applications in various industries, such as surveillance [2], healthcare [3], eldercare [4], sports [1,5], entertainment [6], and beyond [7].…”

Section: Introductionmentioning

confidence: 99%

Action Recognition in Videos Through a Transfer Learning Based Technique

López-Lozada,

Sossa,

Rubio-Espino

et al. 2024

Preprint

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In computer vision, human action recognition is a hot topic, popularized by the development of deep learning. Current models have achieved high accuracy results on public datasets. Despite this success, they require significant computational resources for training. Given that transfer learning based techniques allow reusing what other models have already learned and training models with less computational resources, in this work we propose using a transfer learning based approach for action recognition in videos. We describe a methodology for human action recognition using transfer learning techniques in a custom dataset. The proposed method consists of four stages: 1) human detection and tracking, 2) video preprocessing, 3) feature extraction (using pretrained models with ImageNet), and 4) action recognition using a two-stream model consisting of TCNs, LSTMs, and CNNs layers. The custom dataset is imbalanced with 189, 390, 490, 854, and 890 videos per class, respectively. For feature extraction, we analyzed the performance of seven pretrained models: Inception-v3, MobileNet-v2, MobileNet-v3-L, VGG-16, VGG-19, Xception, and ConvNeXt-L. We show that the best results were obtained with the last one. Finally, using pretrained models for feature extraction allowed training in a PC with a single GPU with an accuracy of 94.9%.

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Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation

Cited by 4 publications

References 42 publications

Vision-Based Recognition of Human Motion Intent during Staircase Approaching

Vision-Based Recognition of Human Motion Intent during Staircase Approaching

Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions

Action Recognition in Videos Through a Transfer Learning Based Technique

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