Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people

Ganea, R.; Paraschiv-Ionescu, Anisoara; Büla, Christophe; Rochat, Shékina; Aminian, Kamiar

doi:10.1016/j.medengphy.2011.04.015

Cited by 95 publications

(83 citation statements)

References 47 publications

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“…Automatic activity recognition and quantification systems that utilize inertial sensors are proposed for long-term health and fitness monitoring [10], [11], assessment of mobility in elderly and people with Parkinson's disease [12]- [14], automatic fall detection [9], [15], and rehabilitation [16], [17]. Approaches for automatic activity recognition used by researchers vary in number, type, and placement of utilized sensors, as well as in processing of recorded signals.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Timed-Up-and-Go test: A smartphone implementation

Milošević

Jovanov

Milenkovic

2013

2013 IEEE International Conference on Body Sensor Networks

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Abstract-Timed-Up-and-Go (TUG) is a simple, easy to administer, and frequently used test for assessing balance and mobility in elderly and people with Parkinson's disease. An instrumented version of the test (iTUG) has been recently introduced to better quantify subject's movements during the test. The subject is typically instrumented by a dedicated device designed to capture signals from inertial sensors that are later analyzed by healthcare professionals. In this paper we introduce a smartphone application called sTUG that completely automates the iTUG test so it can be performed at home. sTUG captures the subject's movements utilizing smartphone's built-in accelerometer and gyroscope sensors, determines the beginning and the end of the test and quantifies its individual phases, and optionally uploads test descriptors into a medical database. We describe the parameters used to quantify the iTUG test and algorithms to extract the parameters from signals captured by the smartphone sensors.

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Section: Introductionmentioning

confidence: 99%

Quantifying Timed-Up-and-Go test: A smartphone implementation

Milošević

Jovanov

Milenkovic

2013

2013 IEEE International Conference on Body Sensor Networks

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“…A previous study showed that frail elderly subjects exhibited significantly decreased smoothness in the sit-stand transition pattern [8,21,23,[29][30][31][32][33].…”

Section: Discussionmentioning

confidence: 99%

Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis

Zakaria¹

2014

Int J Neurorehabilitation Eng

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This study aimed to show that important fall risk measures among the elderly can be classified using multiple parameters obtained from wearable inertial sensors. The timed up-and-go (TUG) test, a well-known standard assessment test, was used to evaluate the risk of falling among elderly individuals. The use of wearable inertial sensors enables extraction of triaxial acceleration and angular velocity signals for offline analysis. Thirty-eight elderly patients from Fujimoto Hayasuzu Hospital participated in this study. Specific results were provided from the signals obtained from acceleration and angular velocity, and analysis was carried out in each phase of various activities, such as sit-to-stand, walking, etc. Seventy-eight parameters were obtained from the extracted acceleration and angular velocity signals in all phases to classify the risk of falling among the elderly. Using principle component analysis, the most important measures were selected from the gathered parameters. The most influential measure in differentiating subjects with high and low fall risks was the turning angular velocity signal. Methods SubjectsThirty-eight elderly subjects (male, 20; female, 18) with an average age of 65.18 ± 8.90 years from Fujimoto Hayasuzu Hospital, Japan participated in the TUG test. The low-fall-risk (LFR) group comprised 27 subjects, and the high-fall-risk (HFR) group comprised 11 subjects.

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“…Duration is one of the most widely used parameters to evaluate the test's performance [22]. Other parameters have been proposed, such as the movements' smoothness [23] or the duration of the single phases (sit to stand and stand to sit) [24]. Even though these parameters may lead to better results, they are more difficult to estimate than overall duration; hence, they are more likely to introduce a measurement error.…”

Section: Pain and Stiffness Prediction Modelmentioning

confidence: 99%

Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness

et al. 2018

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Background: Evaluation of pain and stiffness in patients with arthritis is largely based on participants retrospectively reporting their self-perceived pain/stiffness. This is subjective and may not accurately reflect the true impact of therapeutic interventions. We now have access to sensor-based systems to continuously capture objective information regarding movement and activity. Objectives: We present an observational study aimed to collect sensor data from participants monitored while performing an unsupervised version of a standard motor task, known as the Five Times Sit to Stand (5×STS) test. The first objective was to explore whether the participants would perform the test regularly in their home environment, and do so in a correct and consistent manner. The second objective was to demonstrate that the measurements collected would enable us to derive an objective signal related to morning pain and stiffness. Methods: We recruited a total of 45 participants, of whom 30 participants fulfilled pre-defined criteria for osteoarthritis, rheumatoid arthritis, or psoriatic arthritis and 15 participants were healthy volunteers. All participants wore accelerometers on their wrists, day and night for about 4 weeks. The participants were asked to perform the 5×STS test in their own home environment at the same time in the morning 3 times per week. We investigated the relationship between pain/stiffness and measurements collected during the 5×STS test by comparing the 5×STS test duration with the patient-reported outcome (PRO) questionnaires, filled in via a smartphone. Results: During the study, we successfully captured accelerometer data from each participant for a period of 4 weeks. The participants performed 56% of the prescribed 5×STS tests. We observed that different tests made by the same participants were performed with subject-specific characteristics that remained consistent throughout the study. We showed that 5×STS test duration (the time taken to complete the 5×STS test) was significantly and robustly associated with the pain and stiffness intensity reported via the PROs, particularly the questions asked in the morning. Conclusions: This study demonstrates the feasibility and usefulness of regular, sensor-based, monitored, unsupervised physical tests to objectively assess the impact of disease on function in the home environment. This approach may permit remote disease monitoring in clinical trials and support the development of novel endpoints from passively collected actigraphy data.

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Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people

Cited by 95 publications

References 47 publications

Quantifying Timed-Up-and-Go test: A smartphone implementation

Quantifying Timed-Up-and-Go test: A smartphone implementation

Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis

Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness

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