Compensation due to age-related decline in sit-to-stand and sit-to-walk

Kruk, Eline van der; Silverman, Anne K.; Reilly, Peter; Bull, Anthony M.J.

doi:10.1016/j.jbiomech.2021.110411

Cited by 39 publications

(78 citation statements)

References 61 publications

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“…Participants were asked to keep their hands in a comfortable position, but to avoid contacting external surfaces to provide extra leverage during the STSTS trials. Research has shown that a proportion of elderly and impaired users prefer to use external surfaces, such as chair arms or their own knees, as compensatory behaviours [36], but in contrast, some stroke rehabilitation trials have chosen to prohibit the use of these techniques, resulting in improved strength and function of the lower limbs [31,32], helping to support the reason for using these limitations in the current study.…”

Section: Experimental Protocolmentioning

confidence: 87%

A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance

Bennett

Kumar

Garate

2022

Sensors

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Sit-to-stand and stand-to-sit transfers are fundamental daily motions that enable all other types of ambulation and gait. However, the ability to perform these motions can be severely impaired by different factors, such as the occurrence of a stroke, limiting the ability to engage in other daily activities. This study presents the recording and analysis of a comprehensive database of full body biomechanics and force data captured during sit-to-stand-to-sit movements in subjects who have and have not experienced stroke. These data were then used in conjunction with simple machine learning algorithms to predict vertical motion trajectories that could be further employed for the control of an assistive robot. A total of 30 people (including 6 with stroke) each performed 20 sit-to-stand-to-sit actions at two different seat heights, from which average trajectories were created. Weighted k-nearest neighbours and linear regression models were then used on two different sets of key participant parameters (height and weight, and BMI and age), to produce a predicted trajectory. Resulting trajectories matched the true ones for non-stroke subjects with an average R2 score of 0.864±0.134 using k = 3 and 100% seat height when using height and weight parameters. Even among a small sample of stroke patients, balance and motion trends were noticed along with a large within-class variation, showing that larger scale trials need to be run to obtain significant results. The full dataset of sit-to-stand-to-sit actions for each user is made publicly available for further research.

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Section: Experimental Protocolmentioning

confidence: 87%

A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance

Bennett

Kumar

Garate

2022

Sensors

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“…Non-age-related kinematics and kinetics are the features in better consistency between the healthy elderly and young adults and are essential to reflect gait in normal function. Due to the lack of neuromusculoskeletal capacity, the elderly tend to adopt compensation strategies with altered muscle recruitment in movement [ 29 , 30 ], which would lead to some age-related changes in kinematic and kinetic features. Considering this, those unchanged non-age-related features are critical in performing normal gait, because they are the results of compensation.…”

Section: Discussionmentioning

confidence: 99%

Non-age-related gait kinematics and kinetics in the elderly

Liang

et al. 2022

BMC Musculoskelet Disord

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Background The change of gait kinematics and kinetics along aging were reported to indicate age-related gait patterns. However, few studies focus on non-age-related gait analysis. This study aims to explore the non-age-related gait kinematics and kinetics by comparing gait analysis outcomes among the healthy elderly and young subjects. Methods Gait analysis at self-paced was conducted on 12 healthy young subjects and 8 healthy elderly subjects. Kinematic and kinetic features of ankle, knee and hip joints were analyzed and compared in two groups. The degree of variation between the young and elderly in each kinematic or kinetic feature was calculated from pattern distance and percentage of significant difference. The k-means clustering and Elbow Method were applied to select and validate non-age-related features. The average waveforms with standard deviation were plotted for the comparison of the results. Results A total of five kinematic and five kinetic features were analyzed on ankle, knee and hip joints in healthy young and elderly groups. The degrees of variation in ankle moment, knee angle, hip flexion angle, and hip adduction moment were 0.1074, 0.1593, 0.1407, and 0.1593, respectively. The turning point was where the k value equals two. The clustering centers were 0.1417 and 0.3691, and the two critical values closest to the cutoff were 0.1593 and 0.3037. The average waveforms of the kinematic or kinetic features mentioned above were highly overlapped with a minor standard deviation between the healthy young and elderly but showed larger variations between the healthy and abnormal. Conclusions The cluster with a minor degree of variation in kinematic and kinetic features between the young and elderly were identified as non-age-related, including ankle moment, knee angle, hip flexion angle, and hip adduction moment. Non-age-related gait kinematics and kinetics are essential indicators for gait with normal function, which is essential in the evaluation of mobility and functional ability of the elderly, and data fusion of the assistant device.

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“…Based on this knowledge and with the goal of sampling the wide set of existing STS impairments and assembling a set of distinct movement classes, we identified six STS patterns-see Table 1. Some of these were normal (P1) and others impaired, based on STS impairment literature (P3, P5, P6) (Riley et al, 1997;Scarborough et al, 2007;Peng et al, 2011;Millor et al, 2014;van Der Kruk et al, 2021) or our consultations with physiotherapists (P2, P4). However, the descriptions in Table 1 and experiment materials were derived from metronome-timed recordings of simulated movements by a healthy individual (the first author).…”

Section: Sts Movement Materialsmentioning

confidence: 99%

An Embodied Sonification Model for Sit-to-Stand Transfers

2022

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Interactive sonification of biomechanical quantities is gaining relevance as a motor learning aid in movement rehabilitation, as well as a monitoring tool. However, existing gaps in sonification research (issues related to meaning, aesthetics, and clinical effects) have prevented its widespread recognition and adoption in such applications. The incorporation of embodied principles and musical structures in sonification design has gradually become popular, particularly in applications related to human movement. In this study, we propose a general sonification model for the sit-to-stand (STS) transfer, an important activity of daily living. The model contains a fixed component independent of the use-case, which represents the rising motion of the body as an ascending melody using the physical model of a flute. In addition, a flexible component concurrently sonifies STS features of clinical interest in a particular rehabilitative/monitoring situation. Here, we chose to represent shank angular jerk and movement stoppages (freezes), through perceptually salient pitch modulations and bell sounds. We outline the details of our technical implementation of the model. We evaluated the model by means of a listening test experiment with 25 healthy participants, who were asked to identify six normal and simulated impaired STS patterns from sonified versions containing various combinations of the constituent mappings of the model. Overall, we found that the participants were able to classify the patterns accurately (86.67 ± 14.69% correct responses with the full model, 71.56% overall), confidently (64.95 ± 16.52% self-reported rating), and in a timely manner (response time: 4.28 ± 1.52 s). The amount of sonified kinematic information significantly impacted classification accuracy. The six STS patterns were also classified with significantly different accuracy depending on their kinematic characteristics. Learning effects were seen in the form of increased accuracy and confidence with repeated exposure to the sound sequences. We found no significant accuracy differences based on the participants' level of music training. Overall, we see our model as a concrete conceptual and technical starting point for STS sonification design catering to rehabilitative and clinical monitoring applications.

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Compensation due to age-related decline in sit-to-stand and sit-to-walk

Cited by 39 publications

References 61 publications

A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance

A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance

Non-age-related gait kinematics and kinetics in the elderly

An Embodied Sonification Model for Sit-to-Stand Transfers

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