Stroke patient daily activity observation system

Collins, Jaired; Warren, Joseph B.; Ma, Mengxuan; Proffitt, Rachel; Skubic, Marjorie

doi:10.1109/bibm.2017.8217765

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…In our prior research, we explored the most appropriate sensor setup location and angle that would ensure the optimal collection of data for action recognition [ 60 , 64 , 65 ]. Additionally, we developed an initial set of actions and tested early versions of the algorithm on healthy individuals.…”

Section: Methodsmentioning

confidence: 99%

“…The dataset included 28 actions in 5 categories, including washing, meal preparation, gadget manipulation, general picking tasks, and walking. We applied the HON4D [ 42 ] algorithm to recognize actions from manually segmented depth videos in the first version [ 60 ]. In the second version, an ensemble network was proposed to recognize and localize actions in untrimmed depth videos and skeletal joint frames containing continuous unsegmented actions.…”

Section: Methodsmentioning

confidence: 99%

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Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation

Proffitt,

Ma,

Skubic

2023

Sensors

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Those who survive the initial incidence of a stroke experience impacts on daily function. As a part of the rehabilitation process, it is essential for clinicians to monitor patients’ health status and recovery progress accurately and consistently; however, little is known about how patients function in their own homes. Therefore, the goal of this study was to develop, train, and test an algorithm within an ambient, in-home depth sensor system that can classify and quantify home activities of individuals post-stroke. We developed the Daily Activity Recognition and Assessment System (DARAS). A daily action logger was implemented with a Foresite Healthcare depth sensor. Daily activity data were collected from seventeen post-stroke participants’ homes over three months. Given the extensive amount of data, only a portion of the participants’ data was used for this specific analysis. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network, which learns rich spatial-temporal features from both depth and skeletal joint data, fuses the prediction outputs from a customized 3D convolutional–de-convolutional network, customized region convolutional 3D network, and a proposed region hierarchical co-occurrence network. The per-frame precision and per-action precision were 0.819 and 0.838, respectively, on the test set. The outcomes from the DARAS can help clinicians to provide more personalized rehabilitation plans that benefit patients.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation

Proffitt,

Ma,

Skubic

2023

Sensors

Self Cite

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“…However, taking training outside of the lab or clinic poses various challenges, such as timely and accurate outcome evaluation, and in many cases, absence of a rehabilitation expert. Related works tend to use virtual reality or camera-based systems [51]- [54] in support of training in such settings.…”

Section: B Action Recognition For Rehabilitation Purposesmentioning

confidence: 99%

BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applications

Dechemi

Bhakri

Sahin

et al. 2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

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Action recognition is an important component to improve autonomy of physical rehabilitation devices, such as wearable robotic exoskeletons. Existing human action recognition algorithms focus on adult applications rather than pediatric ones. In this paper, we introduce BabyNet, a light-weight (in terms of trainable parameters) network structure to recognize infant reaching action from off-body stationary cameras. We develop an annotated dataset that includes diverse reaches performed while in a sitting posture by different infants in unconstrained environments (e.g., in home settings, etc.). Our approach uses the spatial and temporal connection of annotated bounding boxes to interpret onset and offset of reaching, and to detect a complete reaching action. We evaluate the efficiency of our proposed approach and compare its performance against other learning-based network structures in terms of capability of capturing temporal inter-dependencies and accuracy of detection of reaching onset and offset. Results indicate our BabyNet can attain solid performance in terms of (average) testing accuracy that exceeds that of other larger networks, and can hence serve as a light-weight data-driven framework for video-based infant reaching action recognition.

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“…In contrast, Collins et al achieved to recognize several human actions done by stroke patients with high certainty by employing HON4D as a global descriptor [64]. Nghia et al proposed an algorithm to compute discriminative features, depth of wrist, by building a mapping table between the differences of bone joint depth and head depth as Kinect provided [65].…”

Section: Detectionmentioning

confidence: 99%

Kinect-Based Physiotherapy and Assessment: A Comprehensive Review

Rashid¹,

Suriani²,

Nazari³

2018

IJEECS

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Kinect-based physical rehabilitation grows significantly as a mechanism for clinical assessment and rehabilitation due to its flexibility, low-cost and markerless system for human action capture. It is also an approach to provide convenience for for patients’ exercises continuation at home. In this paper, we discuss a review of the present Kinect-based physiotherapy and assessment for rehabilitation patients to provide an outline of the state of art, limitation and issues of concern as well as suggestion for future work in this approach. The paper is constructed into three main parts. The introduction was discussed on physiotherapy exercises and the limitation of current Kinect-based applications. Next, we also discuss on Kinect Skeleton Joint and Kinect Depth Map features that being used widely nowadays. A concise summary with significant findings of each paper had been tabulate for each feature; Skeleton Joints and Depth Map. Afterwards, we assemble a quite number of classification method that being implemented for activity recognition in past few years.

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Stroke patient daily activity observation system

Cited by 10 publications

References 13 publications

Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation

Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation

BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applications

Kinect-Based Physiotherapy and Assessment: A Comprehensive Review

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