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Liu W, Jia L, Xu L, Yang F, Guo Z, Li J, Zhang D, Liu Y, Xiang H, Cheng H, Hou J, Li S, Li H. Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study. Front Neurol 2024; 15:1368902. [PMID: 38841697 PMCID: PMC11150528 DOI: 10.3389/fneur.2024.1368902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/24/2024] [Indexed: 06/07/2024] Open
Abstract
Background Early neurological deterioration (END) is a frequent complication in patients with perforating artery territory infarction (PAI), leading to poorer outcomes. Therefore, we aimed to apply machine learning (ML) algorithms to predict the occurrence of END in PAI and investigate related risk factors. Methods This retrospective study analyzed a cohort of PAI patients, excluding those with severe stenosis of the parent artery. We included demographic characteristics, clinical features, laboratory data, and imaging variables. Recursive feature elimination with cross-validation (RFECV) was performed to identify critical features. Seven ML algorithms, namely logistic regression, random forest, adaptive boosting, gradient boosting decision tree, histogram-based gradient boosting, extreme gradient boosting, and category boosting, were developed to predict END in PAI patients using these critical features. We compared the accuracy of these models in predicting outcomes. Additionally, SHapley Additive exPlanations (SHAP) values were introduced to interpret the optimal model and assess the significance of input features. Results The study enrolled 1,020 PAI patients with a mean age of 60.46 (range 49.11-71.81) years. Of these, 30.39% were women, and 129 (12.65%) experienced END. RFECV selected 13 critical features, including blood urea nitrogen (BUN), total cholesterol (TC), low-density-lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB), atrial fibrillation, loading dual antiplatelet therapy (DAPT), single antiplatelet therapy (SAPT), argatroban, the basal ganglia, the thalamus, the posterior choroidal arteries, maximal axial infarct diameter (measured at < 15 mm), and stroke subtype. The gradient-boosting decision tree had the highest area under the curve (0.914) among the seven ML algorithms. The SHAP analysis identified apoB as the most significant variable for END. Conclusion Our results suggest that ML algorithms, especially the gradient-boosting decision tree, are effective in predicting the occurrence of END in PAI patients.
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Affiliation(s)
- Wei Liu
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Longbin Jia
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Lina Xu
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Fengbing Yang
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Zixuan Guo
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Jinna Li
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Dandan Zhang
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Yan Liu
- The First Clinical College of Changzhi Medical College, Changzhi, China
| | - Han Xiang
- The First Clinical College of Changzhi Medical College, Changzhi, China
| | - Hongjiang Cheng
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Jing Hou
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Shifang Li
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
| | - Huimin Li
- Department of Neurology, Jincheng People's Hospital, Jincheng, China
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Wang X, Cao J, Zhao Q, Chen M, Luo J, Wang H, Yu L, Tsui KL, Zhao Y. Identifying sensors-based parameters associated with fall risk in community-dwelling older adults: an investigation and interpretation of discriminatory parameters. BMC Geriatr 2024; 24:125. [PMID: 38302872 PMCID: PMC10836006 DOI: 10.1186/s12877-024-04723-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 01/18/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Falls pose a severe threat to the health of older adults worldwide. Determining gait and kinematic parameters that are related to an increased risk of falls is essential for developing effective intervention and fall prevention strategies. This study aimed to investigate the discriminatory parameter, which lay an important basis for developing effective clinical screening tools for identifying high-fall-risk older adults. METHODS Forty-one individuals aged 65 years and above living in the community participated in this study. The older adults were classified as high-fall-risk and low-fall-risk individuals based on their BBS scores. The participants wore an inertial measurement unit (IMU) while conducting the Timed Up and Go (TUG) test. Simultaneously, a depth camera acquired images of the participants' movements during the experiment. After segmenting the data according to subtasks, 142 parameters were extracted from the sensor-based data. A t-test or Mann-Whitney U test was performed on the parameters for distinguishing older adults at high risk of falling. The logistic regression was used to further quantify the role of different parameters in identifying high-fall-risk individuals. Furthermore, we conducted an ablation experiment to explore the complementary information offered by the two sensors. RESULTS Fifteen participants were defined as high-fall-risk individuals, while twenty-six were defined as low-fall-risk individuals. 17 parameters were tested for significance with p-values less than 0.05. Some of these parameters, such as the usage of walking assistance, maximum angular velocity around the yaw axis during turn-to-sit, and step length, exhibit the greatest discriminatory abilities in identifying high-fall-risk individuals. Additionally, combining features from both devices for fall risk assessment resulted in a higher AUC of 0.882 compared to using each device separately. CONCLUSIONS Utilizing different types of sensors can offer more comprehensive information. Interpreting parameters to physiology provides deeper insights into the identification of high-fall-risk individuals. High-fall-risk individuals typically exhibited a cautious gait, such as larger step width and shorter step length during walking. Besides, we identified some abnormal gait patterns of high-fall-risk individuals compared to low-fall-risk individuals, such as less knee flexion and a tendency to tilt the pelvis forward during turning.
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Affiliation(s)
- Xuan Wang
- Intelligent Sensing and Proactive Health Research Center, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Junjie Cao
- Intelligent Sensing and Proactive Health Research Center, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Qizheng Zhao
- Intelligent Sensing and Proactive Health Research Center, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Manting Chen
- Intelligent Sensing and Proactive Health Research Center, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Jiajia Luo
- Intelligent Sensing and Proactive Health Research Center, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Hailiang Wang
- School of Design, the Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Lisha Yu
- School of Design, the Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - Kwok-Leung Tsui
- Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Yang Zhao
- Intelligent Sensing and Proactive Health Research Center, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.
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Noamani A, Riahi N, Vette AH, Rouhani H. Clinical Static Balance Assessment: A Narrative Review of Traditional and IMU-Based Posturography in Older Adults and Individuals with Incomplete Spinal Cord Injury. SENSORS (BASEL, SWITZERLAND) 2023; 23:8881. [PMID: 37960580 PMCID: PMC10650039 DOI: 10.3390/s23218881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/26/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
Maintaining a stable upright posture is essential for performing activities of daily living, and impaired standing balance may impact an individual's quality of life. Therefore, accurate and sensitive methods for assessing static balance are crucial for identifying balance impairments, understanding the underlying mechanisms of the balance deficiencies, and developing targeted interventions to improve standing balance and prevent falls. This review paper first explores the methods to quantify standing balance. Then, it reviews traditional posturography and recent advancements in using wearable inertial measurement units (IMUs) to assess static balance in two populations: older adults and those with incomplete spinal cord injury (iSCI). The inclusion of these two groups is supported by their large representation among individuals with balance impairments. Also, each group exhibits distinct aspects in balance assessment due to diverse underlying causes associated with aging and neurological impairment. Given the high vulnerability of both demographics to balance impairments and falls, the significance of targeted interventions to improve standing balance and mitigate fall risk becomes apparent. Overall, this review highlights the importance of static balance assessment and the potential of emerging methods and technologies to improve our understanding of postural control in different populations.
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Affiliation(s)
- Alireza Noamani
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (A.N.); (N.R.); (A.H.V.)
| | - Negar Riahi
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (A.N.); (N.R.); (A.H.V.)
| | - Albert H. Vette
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (A.N.); (N.R.); (A.H.V.)
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
- Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB T5G 0B7, Canada
| | - Hossein Rouhani
- Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; (A.N.); (N.R.); (A.H.V.)
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
- Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB T5G 0B7, Canada
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Bohlke K, Redfern MS, Rosso AL, Sejdic E. Accelerometry applications and methods to assess standing balance in older adults and mobility-limited patient populations: a narrative review. Aging Clin Exp Res 2023; 35:1991-2007. [PMID: 37526887 PMCID: PMC10881067 DOI: 10.1007/s40520-023-02503-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/11/2023] [Indexed: 08/02/2023]
Abstract
Accelerometers provide an opportunity to expand standing balance assessments outside of the laboratory. The purpose of this narrative review is to show that accelerometers are accurate, objective, and accessible tools for balance assessment. Accelerometry has been validated against current gold standard technology, such as optical motion capture systems and force plates. Many studies have been conducted to show how accelerometers can be useful for clinical examinations. Recent studies have begun to apply classification algorithms to accelerometry balance measures to discriminate populations at risk for falls. In addition to healthy older adults, accelerometry can monitor balance in patient populations such as Parkinson's disease, multiple sclerosis, and traumatic brain injury. The lack of software packages or easy-to-use applications have hindered the shift into the clinical space. Lack of consensus on outcome metrics has also slowed the clinical adoption of accelerometer-based balance assessments. Future studies should focus on metrics that are most helpful to evaluate balance in specific populations and protocols that are clinically efficacious.
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Affiliation(s)
- Kayla Bohlke
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Mark S Redfern
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Andrea L Rosso
- Department of Epidemiology, School of Public Health, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Ervin Sejdic
- The Edward S. Rogers Department of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, M5S, Canada.
- North York General Hospital, 4001 Leslie St., Toronto, ON, M2K, Canada.
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Zhao Y, Yu L, Fan X, Pang MYC, Tsui KL, Wang H. Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:8008. [PMID: 37766060 PMCID: PMC10535689 DOI: 10.3390/s23188008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users' multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.
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Affiliation(s)
- Yang Zhao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China;
| | - Lisha Yu
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China;
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518000, China;
| | - Marco Y. C. Pang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China;
| | - Kwok-Leung Tsui
- Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA;
| | - Hailiang Wang
- School of Design, The Hong Kong Polytechnic University, Hong Kong, China;
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Ortega-Bastidas P, Gómez B, Aqueveque P, Luarte-Martínez S, Cano-de-la-Cuerda R. Instrumented Timed Up and Go Test (iTUG)-More Than Assessing Time to Predict Falls: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3426. [PMID: 37050485 PMCID: PMC10098780 DOI: 10.3390/s23073426] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 06/19/2023]
Abstract
The Timed Up and Go (TUG) test is a widely used tool for assessing the risk of falls in older adults. However, to increase the test's predictive value, the instrumented Timed Up and Go (iTUG) test has been developed, incorporating different technological approaches. This systematic review aims to explore the evidence of the technological proposal for the segmentation and analysis of iTUG in elderlies with or without pathologies. A search was conducted in five major databases, following PRISMA guidelines. The review included 40 studies that met the eligibility criteria. The most used technology was inertial sensors (75% of the studies), with healthy elderlies (35%) and elderlies with Parkinson's disease (32.5%) being the most analyzed participants. In total, 97.5% of the studies applied automatic segmentation using rule-based algorithms. The iTUG test offers an economical and accessible alternative to increase the predictive value of TUG, identifying different variables, and can be used in clinical, community, and home settings.
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Affiliation(s)
- Paulina Ortega-Bastidas
- Health Sciences PhD Programme, International Doctoral School, Universidad Rey Juan Carlos, 28922 Madrid, Spain
- Kinesiology Department, Faculty of Medicine, Universidad de Concepción, Concepción, 151 Janequeo St., Concepcion 4030000, Chile
| | - Britam Gómez
- Biomedical Engineering, Faculty of Engineering, Universidad de Santiago de Chile, Libertador Bernardo O’Higgins Av., Santiago 9170022, Chile
| | - Pablo Aqueveque
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, 219 Edmundo Larenas St., Concepción 4030000, Chile
| | - Soledad Luarte-Martínez
- Kinesiology Department, Faculty of Medicine, Universidad de Concepción, Concepción, 151 Janequeo St., Concepcion 4030000, Chile
| | - Roberto Cano-de-la-Cuerda
- Physiotherapy, Occupational Therapy, Rehabilitation and Physical Medicine Department, Universidad Rey Juan Carlos, 28922 Madrid, Spain
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Chen M, Wang H, Yu L, Yeung EHK, Luo J, Tsui KL, Zhao Y. A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults. SENSORS (BASEL, SWITZERLAND) 2022; 22:6752. [PMID: 36146103 PMCID: PMC9504041 DOI: 10.3390/s22186752] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/21/2022] [Accepted: 09/02/2022] [Indexed: 06/16/2023]
Abstract
Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.
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Affiliation(s)
- Manting Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China
| | - Hailiang Wang
- School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Lisha Yu
- Shenzhen Enstech Technology Co., Ltd., Shenzhen 518000, China
| | - Eric Hiu Kwong Yeung
- Department of Physiotherapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518000, China
| | - Jiajia Luo
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China
| | - Kwok-Leung Tsui
- Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Yang Zhao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, China
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Noamani A, Vette AH, Rouhani H. Instrumented Functional Test for Objective Outcome Evaluation of Balance Rehabilitation in Elderly Fallers: A Clinical Study. Gerontology 2022; 68:1233-1245. [DOI: 10.1159/000521001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/17/2021] [Indexed: 11/19/2022] Open
Abstract
<b><i>Introduction:</i></b> Observational tests, e.g., the Berg Balance Scale (BBS) are widely used for balance evaluation in the elderly fallers. However, they do not allow objective outcome evaluation of rehabilitative interventions. This study aimed to investigate, in a clinical setting, the use of inertial measurement units (IMUs) integrated into the BBS test for objective outcome evaluation of balance rehabilitation in elderly fallers compared to conventional BBS scores. <b><i>Methods:</i></b> Thirty-six elderly fallers were recruited from the in-patient population of a geriatrics Clinic. Participants performed the BBS test while wearing 3 IMUs placed on the sternum, sacrum, and tibia of the dominant leg following admission to the clinic. Subsequently, they completed a rehabilitation program for 2–4 weeks. They performed a similar test before their discharge. The physical therapist recorded the BBS scores at both sessions, and the sensor data of the 2-min quiet standing task (BBS task 2) were extracted for objective balance evaluation. Moreover, eleven young adults were recruited to perform a 2-min quiet standing test while wearing the same IMUs. Center-of-pressure (COP) and segmental center-of-mass (COM) accelerations were calculated to estimate time-domain, frequency-domain, and intersegment coordination biomarkers of balance. <b><i>Results:</i></b> COP time- and frequency-domain measures, COM acceleration time-domain measures, and intersegment coordination measures could identify age-related changes in balance of seniors compared to young adults (<i>p</i> < 0.05). Moreover, balance biomarkers of senior adults exhibited a reduced sway acceleration and jerkiness in the medial-lateral direction post-rehabilitation (<i>p</i> < 0.05). Although the total BBS scores increased post-rehabilitation, sway displacement and velocity did not significantly improve. We observed a significant association between pelvis-leg coordination at high sway oscillations and the total BBS scores pre- and post-rehabilitation. <b><i>Conclusion:</i></b> IMUs enable not only the characterization of underlying causes of impaired balance but also the identification of improved and yet impaired aspects of balance post-rehabilitation. Hence, IMUs allow us to characterize risk factors post-rehabilitation in elderly fallers, whereas the BBS scores only show changes in overall balance. It is crucial to objectively evaluate the effectiveness of such interventions to reduce future falls and their adverse consequences. Therefore, instrumented balance assessment is recommended since it can provide quantitative and objective measures for clinical outcome evaluations.
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