scholarly journals Automated movement assessment in stroke rehabilitation

2021 ◽  
Author(s):  
Tamim Ahmed ◽  
Kowshik Thopalli ◽  
Thanassis Rikakis ◽  
Pavan Turaga ◽  
Aisling Kelliher ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Hierarchical Model ◽  
Stroke Rehabilitation ◽  
Expert Knowledge ◽  
Low Cost ◽  
Data Driven ◽  
Task Completion ◽  
Convolutional Network ◽  
Rgb Images ◽  
High Level

We are developing a system for long-term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high-level constraints relating to activity structure (i.e. type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high-level priors to data-driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data-driven techniques. We use a transformer-based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complementary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce robust segmentation and task assessment results on noisy, variable, and limited data, which is characteristic of low-cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification, and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e. lower extremity training for neurological accidents).

scholarly journals Automated Movement Assessment in Stroke Rehabilitation

2021 ◽  
Vol 12 ◽  
Author(s):  
Tamim Ahmed ◽  
Kowshik Thopalli ◽  
Thanassis Rikakis ◽  
Pavan Turaga ◽  
Aisling Kelliher ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Hierarchical Model ◽  
Stroke Rehabilitation ◽  
Expert Knowledge ◽  
Low Cost ◽  
Data Driven ◽  
Task Completion ◽  
Convolutional Network ◽  
Rgb Images ◽  
High Level

We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e., type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e., lower extremity training for neurological accidents).

scholarly journals Qualitative and Quantitative Integrated Modeling for Stochastic Simulation and Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Xuefeng Yan ◽  
Yong Zhou ◽  
Yan Wen ◽  
Xudong Chai
Keyword(s):  
Hierarchical Model ◽  
Description Logic ◽  
Expert Knowledge ◽  
Uncertain Reasoning ◽  
Model Structure ◽  
Meta Model ◽  
Simulation And Optimization ◽  
Qualitative And Quantitative ◽  
Level Model ◽  
High Level

The simulation and optimization of an actual physics system are usually constructed based on the stochastic models, which have both qualitative and quantitative characteristics inherently. Most modeling specifications and frameworks find it difficult to describe the qualitative model directly. In order to deal with the expert knowledge, uncertain reasoning, and other qualitative information, a qualitative and quantitative combined modeling specification was proposed based on a hierarchical model structure framework. The new modeling approach is based on a hierarchical model structure which includes the meta-meta model, the meta-model and the high-level model. A description logic system is defined for formal definition and verification of the new modeling specification. A stochastic defense simulation was developed to illustrate how to model the system and optimize the result. The result shows that the proposed method can describe the complex system more comprehensively, and the survival probability of the target is higher by introducing qualitative models into quantitative simulation.

scholarly journals Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6841
Author(s):  
Sergio Cofre-Martel ◽  
Enrique Lopez Droguett ◽  
Mohammad Modarres
Keyword(s):  
Complex Systems ◽  
Expert Knowledge ◽  
Health Management ◽  
Low Cost ◽  
Data Preprocessing ◽  
Data Driven ◽  
Data Sets ◽  
Health State ◽  
Prognostics And Health Management ◽  
Monitoring Networks

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen
Keyword(s):  
Remote Sensing ◽  
Scene Understanding ◽  
Deep Understanding ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Scene Graph ◽  
Multi Scale ◽  
Relationship Extraction ◽  
High Level ◽  
Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

A gaming system with haptic feedback to improve upper extremity function: A prospective case series

2021 ◽  
pp. 1-12
Author(s):  
Hamilton Hernandez ◽  
Isabelle Poitras ◽  
Linda Fay ◽  
Ajmal Khan ◽  
Jean-Sébastien Roy ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Haptic Feedback ◽  
Low Cost ◽  
Case Series ◽  
Performance Measure ◽  
Occupational Performance ◽  
Canadian Occupational Performance Measure ◽  
Prospective Case Series ◽  
Paediatric Rehabilitation ◽  
Therapeutic Settings

BACKGROUND: Video games can be used to motivate repetitive movements in paediatric rehabilitation. Most upper limb videogaming therapies do not however include haptic feedback which can limit their impact. OBJECTIVE: To explore the effectiveness of interactive computer play with haptic feedback for improving arm function in children with cerebral palsy (CP). METHODS: Eleven children with hemiplegic CP attended 12 therapist-guided sessions in which they used a gaming station composed of the Novint Falcon, custom-built handles, physical supports for the child’s arm, games, and an application to manage and calibrate therapeutic settings. Outcome measures included Quality of Upper Extremity Skills Test (QUEST) and Canadian Occupational Performance Measure (COPM). The study protocol is registered on clinicaltrials.gov (NCT04298411). RESULTS: Participants completed a mean of 3858 wrist extensions and 6665 elbow/shoulder movements during the therapist-guided sessions. Clinically important improvements were observed on the dissociated and grasp dimensions on the QUEST and the performance and satisfaction scales of the COPM (all p< 0.05). CONCLUSION: This study suggests that computer play with haptic feedback could be a useful and playful option to help improve the hand/arm capacities of children with CP and warrants further study. The opportunities and challenges of using low-cost, mainstream gaming software and hardware for therapeutic applications are discussed.

scholarly journals Hybrid mosquitoes? Evidence from rural Tanzania on how local communities conceptualize and respond to modified mosquitoes as a tool for malaria control

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Marceline F. Finda ◽  
Fredros O. Okumu ◽  
Elihaika Minja ◽  
Rukiyah Njalambaha ◽  
Winfrida Mponzi ◽  
...  
Keyword(s):  
Malaria Control ◽  
Technology Development ◽  
Low Cost ◽  
Public Support ◽  
Community Leaders ◽  
Community Perceptions ◽  
Novel Technologies ◽  
Control Programmes ◽  
High Level ◽  
Interpretive Frames

Abstract Background Different forms of mosquito modifications are being considered as potential high-impact and low-cost tools for future malaria control in Africa. Although still under evaluation, the eventual success of these technologies will require high-level public acceptance. Understanding prevailing community perceptions of mosquito modification is, therefore, crucial for effective design and implementation of these interventions. This study investigated community perceptions regarding genetically-modified mosquitoes (GMMs) and their potential for malaria control in Tanzanian villages where no research or campaign for such technologies has yet been undertaken. Methods A mixed-methods design was used, involving: (i) focus group discussions (FGD) with community leaders to get insights on how they frame and would respond to GMMs, and (ii) structured questionnaires administered to 490 community members to assess awareness, perceptions and support for GMMs for malaria control. Descriptive statistics were used to summarize the findings and thematic content analysis was used to identify key concepts and interpret the findings. Results Nearly all survey respondents were unaware of mosquito modification technologies for malaria control (94.3%), and reported no knowledge of their specific characteristics (97.3%). However, community leaders participating in FGDs offered a set of distinctive interpretive frames to conceptualize interventions relying on GMMs for malaria control. The participants commonly referenced their experiences of cross-breeding for selecting preferred traits in domestic plants and animals. Preferred GMMs attributes included the expected reductions in insecticide use and human labour. Population suppression approaches, requiring as few releases as possible, were favoured. Common concerns included whether the GMMs would look or behave differently than wild mosquitoes, and how the technology would be integrated into current malaria control policies. The participants emphasised the importance and the challenge of educating and engaging communities during the technology development. Conclusions Understanding how communities perceive and interpret novel technologies is crucial to the design and effective implementation of new vector control programmes. This study offers vital clues on how communities with no prior experience of modified mosquitoes might conceptualize or respond to such technologies when deployed in the context of malaria control programmes. Drawing upon existing interpretive frames and locally-resonant analogies when deploying such technologies may provide a basis for more durable public support in the future.

Hybrid-Data Approach for Estimating Trip Purposes

2021 ◽  
pp. 036119812110184
Author(s):  
Xiaoling Luo ◽  
Adrian Cottam ◽  
Yao-Jan Wu ◽  
Yangsheng Jiang
Keyword(s):  
Transportation Systems ◽  
Data Driven ◽  
Trip Purpose ◽  
Additional Information ◽  
Point Of Interest ◽  
Average Accuracy ◽  
Hybrid Data ◽  
Added Benefit ◽  
Data Source ◽  
High Level

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

Cost-Effective Directional Drilling and Logging-While-Drilling Operational/Maintenance Model Aids an East Africa Operator to Deliver Its Remote Location Exploration Campaign

2016 ◽  
Author(s):  
Iain Lunney
Keyword(s):  
Low Cost ◽  
Commodity Prices ◽  
Cost Effective ◽  
Directional Drilling ◽  
Remote Maintenance ◽  
Support Team ◽  
High Level ◽  
Remote Assistance ◽  
Level Of Experience ◽  
Maintenance Model

ABSTRACT In a cost-sensitive market driven by depressed commodity prices, significant capital challenges exist for operators interested in pursuing exploration activities in remote environments to define their producible reserves. This paper explores the organizational and operational model developed by a service company over several remote area mobilizations; this model resulted in an optimized low-cost service delivery model characterized by top quartile operational key performance indicators (KPIs). The model centralizes critical functions of an operational organization into discrete service units that are located near the operational location or that provide remote assistance with communication and reporting lines in place to function effectively. Top quartile operational performance and tool availability is a result of placing a remote repair and maintenance facility that includes containerized specialty modules near the operational area. The upfront bottomhole assembly engineering, 24/7 monitoring, and proactive feedback of logged data, drillstring dynamics, and wellbore hydraulics are performed by a core team of subject matter experts in their respective disciplines from an established centralized operating center. The operational KPIs over the course of the six well exploration campaign provided substantial evidence to support the reliability of the model and the high level of experience used in both the remote maintenance facility and the operations center support team.

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