Exploration of Machine Learning to Identify Community Dwelling Older Adults with Balance Dysfunction Using Short Duration Accelerometer Data

Hu, Yang; Bishnoi, Alka; Kaur, Rachneet; Sowers, Richard B.; Hernandez, Manuel E.

doi:10.1109/embc44109.2020.9175871

Cited by 9 publications

(12 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methodological quality of the 22 analyzed studies (Table S1 in Multimedia Appendix 1 [ 51 , 52 , 54 , 56 , 58 , 59 , 61 , 63 , 64 , 69 , 70 , 72 ]) ranged from 11 points in 2 (9.1%) studies [ 52 , 65 ] to 16 points in 7 (32%) studies [ 53 , 54 , 56 , 63 , 69 - 71 ].…”

Section: Resultsmentioning

confidence: 99%

“…Finally, regarding the sex of the samples, 13.6% (3/22) of the articles specified in the characteristics of their samples that only female individuals were included among their participants [ 53 , 59 , 70 ].…”

Section: Resultsmentioning

confidence: 99%

“…Regarding the applied assessment procedures, 3 main options stood out: studies with tests or assessments accompanied by sensors or accelerometers [ 51 - 57 , 59 , 61 - 63 , 66 , 70 - 72 ], studies with tests or assessments accompanied by cameras [ 68 , 69 ], or studies based on medical records [ 58 , 60 , 65 , 67 ] ( Figure 2 ). Guillan et al [ 64 ] performed a physical and functional evaluation of the participants.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

González-Castro,

Leirós-Rodríguez,

Prada-García

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. Objective The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. Methods A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. Results We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. Conclusions The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. Trial Registration PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

González-Castro,

Leirós-Rodríguez,

Prada-García

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The effects of somatosensory impairment are characterized by decreased foot sensation [3], changes in the relation between muscle strength and gait speed [4], and position sense deficits in the lower limbs [5]. In addition, PwMS have altered temporal-spatial gait patterns [6] as well as increased variability of trunk movement [7] and footfall placement variability [8] during gait, all of which are expected to contribute to the increased likelihood of balance loss and falls [9][10][11][12]. To compensate for the impact of the loss in proprioception on balance, PwMS might have greater reliance on the vestibular or visual systems for balance control [13], as documented by a decrease in stability when visual feedback is removed during standing [14][15][16] and increased sensitivity to visual stimuli during gait [17].…”

Section: Introductionmentioning

confidence: 99%

Visual oscillation effects on dynamic balance control in people with multiple sclerosis

Riem

Beardsley

Obeidat

et al. 2022

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Background People with multiple sclerosis (PwMS) have balance deficits while ambulating through environments that contain moving objects or visual manipulations to perceived self-motion. However, their ability to parse object from self-movement has not been explored. The purpose of this research was to examine the effect of medial–lateral oscillations of the visual field and of objects within the scene on gait in PwMS and healthy age-matched controls using virtual reality (VR). Methods Fourteen PwMS (mean age 49 ± 11 years, functional gait assessment score of 27.8 ± 1.8, and Berg Balance scale score 54.7 ± 1.5) and eleven healthy controls (mean age: 53 ± 12 years) participated in this study. Dynamic balance control was assessed while participants walked on a treadmill at a self-selected speed while wearing a VR headset that projected an immersive forest scene. Visual conditions consisted of (1) no visual manipulations (speed-matched anterior/posterior optical flow), (2) 0.175 m mediolateral translational oscillations of the scene that consisted of low pairing (0.1 and 0.31 Hz) or (3) high pairing (0.15 and 0.465 Hz) frequencies, (4) 5 degree medial–lateral rotational oscillations of virtual trees at a low frequency pairing (0.1 and 0.31 Hz), and (5) a combination of the tree and scene movements in (3) and (4). Results We found that both PwMS and controls exhibited greater instability and visuomotor entrainment to simulated mediolateral translation of the visual field (scene) during treadmill walking. This was demonstrated by significant (p < 0.05) increases in mean step width and variability and center of mass sway. Visuomotor entrainment was demonstrated by high coherence between center of mass sway and visual motion (magnitude square coherence = ~ 0.5 to 0.8). Only PwMS exhibited significantly greater instability (higher step width variability and center of mass sway) when objects moved within the scene (i.e., swaying trees). Conclusion Results suggest the presence of visual motion processing errors in PwMS that reduced dynamic stability. Specifically, object motion (via tree sway) was not effectively parsed from the observer’s self-motion. Identifying this distinction between visual object motion and self-motion detection in MS provides insight regarding stability control in environments with excessive external movement, such as those encountered in daily life.

show abstract

“…The effects of somatosensory impairment are characterized by decreased foot sensation [3], changes in the relation between muscle strength and gait speed [4], and position sense de cits in the lower limbs [5]. In addition, PwMS have altered temporalspatial gait patterns [6] as well as increased variability of trunk movement [7] and footfall placement variability [8] during gait, all of which are expected to contribute to the increased likelihood of balance loss and falls [9][10][11][12]. To compensate for the impact of the loss in proprioception on balance, PwMS might have greater reliance on the vestibular or visual systems for balance control [13], as documented by a decrease in stability when visual feedback is removed during standing [14][15][16] and increased sensitivity to visual stimuli during gait [17].…”

Section: Introductionmentioning

confidence: 99%

Visual oscillation effects on dynamic balance control in people with multiple sclerosis

Riem

Beardsley

Obeidat

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: People with multiple sclerosis (PwMS) have balance deficits while ambulating through environments that contain moving objects or visual manipulations to perceived self-motion. However, their ability to parse object from self-movement has not been explored. The purpose of this research was to examine the effect of medial-lateral oscillations of the visual field and of objects within the scene on gait in PwMS and healthy age-matched controls using virtual reality (VR).Methods: Fourteen PwMS (mean age 49 ± 11 years) and eleven healthy controls (mean age: 53 ± 12 years) participated in this study. Dynamic balance control was assessed while participants walked on a treadmill at a self-selected speed while wearing a VR headset that projected an immersive forest scene. Visual conditions consisted of (1) no visual manipulations (speed-matched anterior/posterior optical flow), (2) 0.175 meter mediolateral translational oscillations of the scene that consisted of low pairing (0.1 and 0.31 Hz) or (3) high pairing (0.15 and 0.465 Hz) frequencies, (4) 5 degree medial-lateral rotational oscillations of virtual trees at a low frequency pairing (0.1 and 0.31 Hz), and (5) a combination of the tree and scene movements in (3) and (4).Results: We found that both PwMS and controls exhibited greater instability and visuomotor entrainment to simulated mediolateral translation of the visual field (scene) during treadmill walking. This was demonstrated by significant (p<0.05) increases in mean step width and variability and center of mass sway. Visuomotor entrainment was demonstrated by high coherence between center of mass sway and visual motion (magnitude square coherence = ~ 0.5 to 0.8). Only PwMS exhibited significantly greater instability (higher step width variability and center of mass sway) when objects moved within the scene (i.e., swaying trees).Conclusion: Results suggest the presence of visual motion processing errors in PwMS that reduced dynamic stability. Specifically, object motion (via tree sway) was not effectively parsed from the observer’s self-motion. Identifying this distinction between visual object motion and self-motion detection in MS provides insight regarding stability control in environments with excessive external movement, such as those encountered in daily life.

show abstract

Exploration of Machine Learning to Identify Community Dwelling Older Adults with Balance Dysfunction Using Short Duration Accelerometer Data

Cited by 9 publications

References 14 publications

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review

Visual oscillation effects on dynamic balance control in people with multiple sclerosis

Visual oscillation effects on dynamic balance control in people with multiple sclerosis

Contact Info

Product

Resources

About