Collaborative design of a telerehabilitation system enabling virtual second opinion based on fuzzy logic

Capecci, Marianna; Ciabattoni, Lucio; Ferracuti, Francesco; Monteriù, Andrea; Romeo, Luca; Verdini, Federica

doi:10.1049/iet-cvi.2017.0114

Cited by 7 publications

(3 citation statements)

References 39 publications

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“…The rule-based approach can exploit the proposed dataset by properly setting the rule parameters (i.e., objective and tolerance value [37] or parameter of the membership function for the fuzzy inference [67]) for each different exercise. This process may be considered as the main core of the collaborative design procedure described in [67].…”

Section: A the Impact Of The Kimore Dataset For Rehabilitation Assess...mentioning

confidence: 99%

The KIMORE Dataset: KInematic Assessment of MOvement and Clinical Scores for Remote Monitoring of Physical REhabilitation

Capecci

Ceravolo

Ferracuti

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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This paper proposes a free dataset, available at the following link, 1 named KIMORE, regarding different rehabilitation exercises collected by a RGB-D sensor. Three data inputs including RGB, depth videos, and skeleton joint positions were recorded during five physical exercises, specific for low back pain and accurately selected by physicians. For each exercise, the dataset also provides a set of features, specifically defined by the physicians, and relevant to describe its scope. These features, validated with respect to a stereophotogrammetric system, can be analyzed to compute a score for the subject's performance. The dataset also contains an evaluation of the same performance provided by the clinicians, through a clinical questionnaire. The impact of KIMORE has been analyzed by comparing the output obtained by an example of rule and template-based approaches and the clinical score. The dataset presented is intended to be used as a benchmark for human movement assessment in a rehabilitation scenario in order to test the effectiveness and the reliability of different computational approaches. Unlike other existing datasets, the KIMORE merges a large heterogeneous population of 78 subjects, divided into 2 groups with 44 healthy subjects and 34 with motor dysfunctions. It provides the most clinically-relevant features and the clinical score for each exercise.

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Section: A the Impact Of The Kimore Dataset For Rehabilitation Assess...mentioning

confidence: 99%

The KIMORE Dataset: KInematic Assessment of MOvement and Clinical Scores for Remote Monitoring of Physical REhabilitation

Capecci

Ceravolo

Ferracuti

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

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“…Traditional motion recognition technology mainly depends on manual feature extraction. The selection of features generally depends on the domain knowledge of experts [40] . For human action recognition, skeleton point extraction is the first step of our pipeline, which is realized via Openpose, a widely used open-source library for key point detection.…”

Section: Model Architecture and Recognition Methodsmentioning

confidence: 99%

CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise

Chen

Wang

Yan

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Identifying human actions from video data is an important problem in the fields of intelligent rehabilitation assessment. Motion feature extraction and pattern recognition are the two key procedures to achieve such goals. Traditional action recognition models are usually based on the geometric features manually extracted from video frames, which are however difficult to adapt to complex scenarios and cannot achieve high-precision recognition and robustness. We investigate a motion recognition model and apply it to recognize the sequence of complicated actions of a traditional Chinese exercise (ie, Baduanjin). We first developed a combined convolutional neural network (CNN) and long short-term memory (LSTM) model for recognizing the sequence of actions captured in video frames, and applied it to recognize the actions of Baduanjin. Moreover, this method has been compared with the traditional action recognition model based on geometric motion features in which Openpose is used to identify the joint positions in the skeletons. Its performance of high recognition accuracy has been verified on the testing video dataset, containing the video clips from 18 different practicers. The CNN-LSTM recognition model achieved 96.43% accuracy on the testing set; while those manually extracted features in the traditional action recognition model were only able to achieve 66.07% classification accuracy on the testing video dataset. The abstract image features extracted by the CNN module are more effective on improving the classification accuracy of the LSTM model. The proposed CNN-LSTM based method can be a useful tool in recognizing the complicated actions.

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“…AI/ML has also been used in epidemiological modelling and forecasting (Lalmuanawma et al, 2020), clinical decision support (Montani and Striani, 2019), and health care operations and logistics (Obermeyer et al, 2019). Involvement of patients or publics in the design of advanced digital technologies often emphasizes the inherent patient-centred or empowering qualities of co-design approaches (Capecci et al, 2018; Enshaeifar et al, 2018; Triberti and Barello, 2016) or AI/ML technologies (Topol, 2019), especially when directed to health-related goals. As such, co-design, PPI, and PFCC afford legitimacy to AI/ML technologies for health, though the extent to which they always should, remains a topic of debate.…”

Section: Involvement In Health Carementioning

confidence: 99%

Co-design and ethical artificial intelligence for health: An agenda for critical research and practice

Donia

Shaw

2021

Big Data & Society

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Applications of artificial intelligence/machine learning (AI/ML) in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ML for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ML introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical data studies and critical digital health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ML for health care. We suggest that AI/ML technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ML for health care and conclude with suggestions for addressing these practical and conceptual challenges.

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Collaborative design of a telerehabilitation system enabling virtual second opinion based on fuzzy logic

Cited by 7 publications

References 39 publications

The KIMORE Dataset: KInematic Assessment of MOvement and Clinical Scores for Remote Monitoring of Physical REhabilitation

The KIMORE Dataset: KInematic Assessment of MOvement and Clinical Scores for Remote Monitoring of Physical REhabilitation

CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise

Co-design and ethical artificial intelligence for health: An agenda for critical research and practice

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