Camera-Based Short Physical Performance Battery and Timed Up and Go Assessment for Older Adults With Cancer

Duncan, Larry; Zhu, Shaotong; Pergolotti, Mackenzi; Giri, Smith; Salsabili, Hoda; Faezipour, Miad; Ostadabbas, Sarah; Mirbozorgi, S. Abdollah

doi:10.1109/tbme.2023.3253061

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…This current chapter is based on and is a continuation of the previously published work, "Camera-Based Short Physical Performance Battery and Timed Up and Go Assessment for Older Adults with Cancer," and one other paper, "Camera-Based Human Gait Speed Monitoring and Tracking for Performance Assessment of Elderly Patients with Cancer [20,21]. "…”

Section: Notesmentioning

confidence: 99%

“…In this work we propose a self-standing Camera-based Body Motion Tracking (CBMT) system, inspired by earlier studies [20,21], and designed for use in clinics and hospitals to enable easier fitness evaluation of patients using the SPPB tests. The CBMT system performs object recognition and tracking of people, accurately processes all SPPB statistics, can be monitored via a cell phone, and allows for report generation and cloud storage for medical professionals' access.…”

Section: Introductionmentioning

confidence: 99%

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Automated Camera-Based Assessment of Short Physical Performance Battery (SPPB) for Older Adults with Cancer

Duncan,

Zhu,

Pergolotti

et al. 2024

Biomedical Engineering

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This chapter introduces a motorized camera system designed for monitoring and evaluating the tests of the Short Physical Performance Battery (SPPB). This system targets physical performance assessment for older patients undergoing cancer treatment. The device is self-contained, incorporating a small computer, three cameras, and two motors. The core algorithms utilize three object recognition techniques: template matching, Haar cascades, and Channel and Spatial Reliability Tracking. To facilitate user interaction, graphical user interfaces (GUIs) are developed on the small computer, enabling test execution and camera adjustments via cell phone and its hotspot. The system delivers precise results, with gait speed tests showing a range of 0.041–1.92 m/s and average speed and distance accuracies exceeding 95%. The standing balance and 5 times sit-stand (5TSS) tests achieve average time accuracies exceeding 97%. This novel camera-based device holds promise in enhancing evaluation of lower body extremity fitness for elderly people receiving cancer health care, offering a reliable and efficient solution for monitoring their progress and well-being.

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Section: Notesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Camera-Based Assessment of Short Physical Performance Battery (SPPB) for Older Adults with Cancer

Duncan,

Zhu,

Pergolotti

et al. 2024

Biomedical Engineering

Self Cite

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“…On the other hand, some investigations use Computer Vision techniques [ 56 , 57 , 58 , 59 , 60 ], while others have gone a step further and implement robots equipped with cameras and/or sensors. In the study described in [ 56 ], a person performs three types of exercises, and it is recorded by a camera located in a robot that follows a rectilinear trajectory, but the results are not correlated with any of the previously exposed tests, and many parameters of interest are not measured.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment

Blasco-García,

García-López,

Jiménez-Muñoz

et al. 2024

Sensors

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The increase in life expectancy, and the consequent growth of the elderly population, represents a major challenge to guarantee adequate health and social care. The proposed system aims to provide a tool that automates the evaluation of gait and balance, essential to prevent falls in older people. Through an RGB-D camera, it is possible to capture and digitally represent certain parameters that describe how users carry out certain human motions and poses. Such individual motions and poses are actually related to items included in many well-known gait and balance evaluation tests. According to that information, therapists, who would not need to be present during the execution of the exercises, evaluate the results of such tests and could issue a diagnosis by storing and analyzing the sequences provided by the developed system. The system was validated in a laboratory scenario, and subsequently a trial was carried out in a nursing home with six residents. Results demonstrate the usefulness of the proposed system and the ease of objectively evaluating the main items of clinical tests by using the parameters calculated from information acquired with the RGB-D sensor. In addition, it lay the future foundations for creating a Cloud-based platform for remote fall risk assessment and its integration with a mobile assistant robot, and for designing Artificial Intelligence models that can detect patterns and identify pathologies for enabling therapists to prevent falls in users under risk.

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“…Furthermore, robots can provide additional information than the current gathered by professionals at sight. Despite the significance of frailty assessments, only a limited number of studies have tackled the challenge of autonomously administering these tests using computer vision techniques [14,18,19,33] or physical sensors [29,31] and even fewer studies involved robots. For instance, Olde et al [25] used a social robot to explain different physical exercises to diminish people's frailty, but a healthcare professional delivered the tests.…”

Section: Introductionmentioning

confidence: 99%

Introducing Social Robots to Assess Frailty in Older Adults

Civit,

Andriella,

Barrue

et al. 2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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Figure 1: In (a), the workflow of actions expected to be performed by the robot, divided into two stages. In step A, the robot searches for a new patient in the waiting room. In step B, it brings the patient to the evaluation room. In step C, the patient performs the tests with the instructions of the robot. In step D, the robot accompanies the patient back to the waiting room. In (b), an illustration of an ambulatory floor with the locations of the above-mentioned steps: the robot is currently at step A and it needs to find the patient in green. In (c), an example of feedback provided by the Temi robot during step C.

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Camera-Based Short Physical Performance Battery and Timed Up and Go Assessment for Older Adults With Cancer

Cited by 5 publications

References 39 publications

Automated Camera-Based Assessment of Short Physical Performance Battery (SPPB) for Older Adults with Cancer

Automated Camera-Based Assessment of Short Physical Performance Battery (SPPB) for Older Adults with Cancer

A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment

Introducing Social Robots to Assess Frailty in Older Adults

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