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

Proffitt, Rachel; Ma, Mengxuan; Skubic, Marjorie

doi:10.3390/s23187872

Cited by 3 publications

(3 citation statements)

References 72 publications

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“…The DARAS algorithm was developed for the DS6-RN depth sensor ( Foresite Healthcare, n.d. ). The development of the algorithm, including training and testing, are described in full elsewhere ( Proffitt et al., 2023a ). Briefly, the DS6-RN depth sensor was installed in the kitchen of study participants and remained in the kitchen for a period of 3 mo.…”

Section: Methodsmentioning

confidence: 99%

An Algorithm for Activity Recognition and Assessment of Adults Poststroke

Proffitt,

Rasmussen,

et al. 2024

The American Journal of Occupational Therapy

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Importance: Stroke is the leading cause of long-term disability in the United States. Providers have no robust tools to objectively and accurately measure the activity of people with stroke living at home. Objective: To explore the integration of validated upper extremity assessments poststroke within an activity recognition system. Design: Exploratory descriptive study using data previously collected over 3 mo to report on algorithm testing and assessment integration. Setting: Data were collected in the homes of community-dwelling participants. Participants: Participants were at least 6 mo poststroke, were able to ambulate with or without an assistive device, and self-reported some difficulty using their arm in everyday activities. Outcomes and Measures: The activity detection algorithm’s accuracy was determined by comparing its activity labels with manual labels. The algorithm integrated assessment by describing the quality of upper extremity movement, which was determined by reporting extent of reach, mean and maximum speed during movement, and smoothness of movement. Results: Sixteen participants (9 women, 7 men) took part in this study, with an average age of 63.38 yr (SD = 12.84). The algorithm was highly accurate in correctly identifying activities, with 87% to 95% accuracy depending on the movement. The algorithm was also able to detect the quality of movement for upper extremity movements. Conclusions and Relevance: The algorithm was able to accurately identify in-kitchen activities performed by adults poststroke. Information about the quality of these movements was also successfully calculated. This algorithm has the potential to supplement clinical assessments in treatment planning and outcomes reporting. Plain-Language Summary: This study shows that clinical algorithms have the potential to inform occupational therapy practice by providing clinically relevant data about the in-home activities of adults poststroke. The algorithm accurately identified activities that were performed in the kitchen by adults poststroke. The algorithm also identified the quality of upper extremity movements of people poststroke who were living at home.

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Section: Methodsmentioning

confidence: 99%

An Algorithm for Activity Recognition and Assessment of Adults Poststroke

Proffitt,

Rasmussen,

et al. 2024

The American Journal of Occupational Therapy

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“…Gesture recognition systems typically consist of hardware components such as cameras or depth sensors (Proffitt et al, 2023) that capture the user's movements in real-time. These devices generate data that is then processed by software, often powered by machine learning algorithms.…”

Section: Virtual Realitymentioning

confidence: 99%

Gesture Recognition in Virtual reality

2023

Psychomachina

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Virtual Reality (VR) provides users with a sensory experience that is close to reality, creating a sense of interaction. It is widely used, and the gesture recognition in VR also has a great effect. Gesture recognition enriches VR using experience and promotes a more direct and natural interaction. Gesture recognition usually employs sensors to collect data from users and machine leaning algorithms to interpret and respond to human activities. Complex gestures need more complex algorithms and more rigorous operations. The reason is that complex gestures mean larger quantity of data. If data is larger, the harder to get robust and effective datasets. Then, features can also become difficult to extract, contributing to misrecognition or unrecognizable. Though machine leaning algorithms are widely used in gesture recognition, there are still some important challenges need to be addressed, like lack of standardization and limitations of availability of diverse and large datasets. However, VR, gesture recognition and machine leaning algorithms all have excellent prospect, because they are in line with the development of the Times and show the progress of science and technology. This paper not only focuses on their advantages but also does not ignore their shortcomings, and looks at them comprehensively.

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“…Discovering inherent patterns of symmetry or asymmetry in movements may provide profound insights into the underlying meaning linked with activities. Assessing the asymmetry in gait and arm movement could be helpful as an indicator of recovery status in stroke rehabilitation [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Residual Network with a CBAM Mechanism for the Recognition of Symmetric and Asymmetric Human Activity Using Wearable Sensors

Mekruksavanich,

Jitpattanakul

2024

Symmetry

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Wearable devices are paramount in health monitoring applications since they provide contextual information to identify and recognize human activities. Although sensor-based human activity recognition (HAR) has been thoroughly examined, prior studies have yet to definitively differentiate between symmetric and asymmetric motions. Determining these movement patterns might provide a more profound understanding of assessing physical activity. The main objective of this research is to investigate the use of wearable motion sensors and deep convolutional neural networks in the analysis of symmetric and asymmetric activities. This study provides a new approach for classifying symmetric and asymmetric motions using a deep residual network incorporating channel and spatial convolutional block attention modules (CBAMs). Two publicly accessible benchmark HAR datasets, which consist of inertial measurements obtained from wrist-worn sensors, are used to assess the model’s efficacy. The model we have presented is subjected to thorough examination and demonstrates exceptional accuracy on both datasets. The ablation experiment examination also demonstrates noteworthy contributions from the residual mappings and CBAMs. The significance of recognizing basic movement symmetries in increasing sensor-based activity identification utilizing wearable devices is shown by the enhanced accuracy and F1-score, especially in asymmetric activities. The technique under consideration can provide activity monitoring with enhanced accuracy and detail, offering prospective advantages in diverse domains like customized healthcare, fitness tracking, and rehabilitation progress evaluation.

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

Cited by 3 publications

References 72 publications

An Algorithm for Activity Recognition and Assessment of Adults Poststroke

An Algorithm for Activity Recognition and Assessment of Adults Poststroke

Gesture Recognition in Virtual reality

Deep Residual Network with a CBAM Mechanism for the Recognition of Symmetric and Asymmetric Human Activity Using Wearable Sensors

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