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Wang H, Hu B, Huang J, Chen L, Yuan M, Tian X, Shi T, Zhao J, Huang W. Predicting the fatigue in Parkinson's disease using inertial sensor gait data and clinical characteristics. Front Neurol 2023; 14:1172320. [PMID: 37388552 PMCID: PMC10303817 DOI: 10.3389/fneur.2023.1172320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/23/2023] [Indexed: 07/01/2023] Open
Abstract
Objectives The study aimed to analyze the clinical features and gait characteristics of patients with Parkinson's disease (PD) who also suffer from fatigue and to develop a model that can help identify fatigue states in the early stages of PD. Methodology A total of 81 PD patients have been enrolled for the Parkinson's Fatigue Scale (PFS-16) assessment and divided into two groups: patients with or without fatigue. Neuropsychological assessments of the two groups, including motor and non-motor symptoms, were collected. The patient's gait characteristics were collected using a wearable inertial sensor device. Results PD patients who experienced fatigue had a more significant impairment of motor symptoms than those who did not, and the experience of fatigue became more pronounced as the disease progressed. Patients with fatigue had more significant mood disorders and sleep disturbances, which can lead to a poorer quality of life. PD patients with fatigue had shorter step lengths, lower velocity, and stride length and increased stride length variability. As for kinematic parameters, PD patients with fatigue had lower shank-forward swing max, trunk-max sagittal angular velocity, and lumbar-max coronal angular velocity than PD patients without fatigue. The binary logistic analysis found that Movement Disorder Society-Unified Parkinson's Disease Rating Scale-I (MDS-UPDRS-I) scores, Hamilton Depression Scale (HAMD) scores, and stride length variability independently predicted fatigue in PD patients. The area under the curve (AUC) of these selected factors in the receiver operating characteristic (ROC) analysis was 0.900. Moreover, HAMD might completely mediate the association between Hamilton Anxiety Scale (HAMA) scores and fatigue (indirect effect: β = 0.032, 95% confidence interval: 0.001-0.062), with a percentage of mediation of 55.46%. Conclusion Combining clinical characteristics and gait cycle parameters, including MDS-UPDRS-I scores, HAMD scores, and stride length variability, can identify PD patients with a high fatigue risk.
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Affiliation(s)
- Hui Wang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Binbin Hu
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Juan Huang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lin Chen
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Min Yuan
- Department of Neurology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Xingfu Tian
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Shi
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jiahao Zhao
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wei Huang
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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Skiba MB, Harker G, Guidarelli C, El-Gohary M, Horak F, Roeland EJ, Silbermann R, Hayes-Lattin B, Winters-Stone K. Using Wearable Inertial Sensors to Assess Mobility of Patients With Hematologic Cancer and Associations With Chemotherapy-Related Symptoms Before Autologous Hematopoietic Stem Cell Transplant: Cross-sectional Study. JMIR Cancer 2022; 8:e39271. [PMID: 36480243 PMCID: PMC9782382 DOI: 10.2196/39271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/29/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Wearable sensors could be a simple way to quantify and characterize mobility in patients with hematologic cancer scheduled to receive autologous hematopoietic stem cell transplant (autoHSCT) and how they may be related to common treatment-related symptoms and side effects of induction chemotherapy. OBJECTIVE We aimed to conduct a cross-sectional study comparing mobility in patients scheduled to receive autoHSCT with that in healthy, age-matched adult controls and determine the relationships between patient mobility and chemotherapy-related symptoms. METHODS Patients scheduled to receive autoHSCT (78/156, 50%) and controls (78/156, 50%) completed the prescribed performance tests using wearable inertial sensors to quantify mobility including turning (turn duration and number of steps), gait (gait speed, stride time, stride time variability, double support time, coronal trunk range of motion, heel strike angle, and distance traveled), and balance (coronal sway, coronal range, coronal velocity, coronal centroidal frequency, sagittal sway, sagittal range, sagittal velocity, and sagittal centroidal frequency). Patients completed the validated patient-reported questionnaires to assess symptoms common to chemotherapy: chemotherapy-induced peripheral neuropathy (Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity subscale), nausea and pain (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire), fatigue (Patient-Reported Outcomes Measurement Information System Fatigue Short Form 8a), vertigo (Vertigo Symptom Scale-short form), and depression (Center for Epidemiological Studies-Depression). Paired, 2-sided t tests were used to compare mobility between patients and controls. Stepwise multivariable linear regression models were used to evaluate associations between patient mobility and symptoms. RESULTS Patients aged 60.3 (SD 10.3) years had significantly worse turning (turn duration; P<.001), gait (gait speed, stride time, stride time variability, double support time, heel strike angle, stride length, and distance traveled; all P<.001), and balance (coronal sway; P<.001, range; P<.001, velocity; P=.02, and frequency; P=.02; and sagittal range; P=.008) than controls. In patients, high nausea was associated with worse stride time variability (ß=.001; P=.005) and heel strike angle (ß=-.088; P=.02). Pain was associated with worse gait speed (ß=-.003; P=.003), stride time variability (ß=.012; P=.02), stride length (ß=-.002; P=.004), and distance traveled (ß=-.786; P=.005). Nausea and pain explained 17% to 33% and 14% to 36% of gait variance measured in patients, respectively. CONCLUSIONS Patients scheduled to receive autoHSCT demonstrated worse mobility in multiple turning, gait, and balance domains compared with controls, potentially related in part to nausea and pain. Wearable inertial sensors used in the clinic setting could provide granular information about mobility before further treatment, which may in turn benefit from rehabilitation or symptom management. Future longitudinal studies are needed to better understand temporal changes in mobility and symptoms across the treatment trajectory to optimally time, design, and implement strategies, to preserve functioning in patients with hematologic cancer in the long term.
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Affiliation(s)
- Meghan B Skiba
- Biobehavioral Health Science Division, College of Nursing, University of Arizona, Tucson, AZ, United States
- The University of Arizona Cancer Center, University of Arizona, Tucson, AZ, United States
| | - Graham Harker
- Department of Neurology, School of Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Carolyn Guidarelli
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Mahmoud El-Gohary
- APDM, Inc, a division of Clario International, Portland, OR, United States
| | - Fay Horak
- Department of Neurology, School of Medicine, Oregon Health & Science University, Portland, OR, United States
- APDM, Inc, a division of Clario International, Portland, OR, United States
| | - Eric J Roeland
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
- Division of Hematology and Medical Oncology, School of Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Rebecca Silbermann
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
- Division of Hematology and Medical Oncology, School of Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Brandon Hayes-Lattin
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
- Division of Hematology and Medical Oncology, School of Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Kerri Winters-Stone
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
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Hsieh CY, Huang HY, Liu KC, Liu CP, Chan CT, Hsu SJP. Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor. Sensors (Basel) 2021; 21:s21093302. [PMID: 34068804 PMCID: PMC8126206 DOI: 10.3390/s21093302] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/07/2021] [Accepted: 05/07/2021] [Indexed: 12/19/2022]
Abstract
Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.
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Affiliation(s)
- Chia-Yeh Hsieh
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-Y.H.); (H.-Y.H.); (C.-P.L.); (C.-T.C.)
| | - Hsiang-Yun Huang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-Y.H.); (H.-Y.H.); (C.-P.L.); (C.-T.C.)
| | - Kai-Chun Liu
- Research Center for Information Technology Innovation, Academia Sinica, Taipei 11529, Taiwan;
| | - Chien-Pin Liu
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-Y.H.); (H.-Y.H.); (C.-P.L.); (C.-T.C.)
| | - Chia-Tai Chan
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-Y.H.); (H.-Y.H.); (C.-P.L.); (C.-T.C.)
| | - Steen Jun-Ping Hsu
- Department of Information Management, Minghsin University of Science and Technology, Hsinchu 30401, Taiwan
- Correspondence:
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Huang HY, Hsieh CY, Liu KC, Hsu SJ, Chan CT. Fluid Intake Monitoring System Using a Wearable Inertial Sensor for Fluid Intake Management. Sensors (Basel) 2020; 20:E6682. [PMID: 33266484 DOI: 10.3390/s20226682] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 11/10/2020] [Accepted: 11/20/2020] [Indexed: 11/26/2022]
Abstract
Fluid intake is important for people to maintain body fluid homeostasis. Inadequate fluid intake leads to negative health consequences, such as headache, dizziness and urolithiasis. However, people in busy lifestyles usually forget to drink sufficient water and neglect the importance of fluid intake. Fluid intake management is important to assist people in adopting individual drinking behaviors. This work aims to propose a fluid intake monitoring system with a wearable inertial sensor using a hierarchical approach to detect drinking activities, recognize sip gestures and estimate fluid intake amount. Additionally, container-dependent amount estimation models are developed due to the influence of containers on fluid intake amount. The proposed fluid intake monitoring system could achieve 94.42% accuracy, 90.17% sensitivity, and 40.11% mean absolute percentage error (MAPE) for drinking detection, gesture spotting and amount estimation, respectively. Particularly, MAPE of amount estimation is improved approximately 10% compared to the typical approaches. The results have demonstrated the feasibility and the effectiveness of the proposed fluid intake monitoring system.
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Davoudi M, Shokouhyan SM, Abedi M, Meftahi N, Rahimi A, Rashedi E, Hoviattalab M, Narimani R, Parnianpour M, Khalaf K. A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings. Sensors (Basel) 2020; 20:E2902. [PMID: 32443827 PMCID: PMC7287918 DOI: 10.3390/s20102902] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/12/2020] [Accepted: 05/18/2020] [Indexed: 11/26/2022]
Abstract
The successful clinical application of patient-specific personalized medicine for the management of low back patients remains elusive. This study aimed to classify chronic nonspecific low back pain (NSLBP) patients using our previously developed and validated wearable inertial sensor (SHARIF-HMIS) for the assessment of trunk kinematic parameters. One hundred NSLBP patients consented to perform repetitive flexural movements in five different planes of motion (PLM): 0° in the sagittal plane, as well as 15° and 30° lateral rotation to the right and left, respectively. They were divided into three subgroups based on the STarT Back Screening Tool. The sensor was placed on the trunk of each patient. An ANOVA mixed model was conducted on the maximum and average angular velocity, linear acceleration and maximum jerk, respectively. The effect of the three-way interaction of Subgroup by direction by PLM on the mean trunk acceleration was significant. Subgrouping by STarT had no main effect on the kinematic indices in the sagittal plane, although significant effects were observed in the asymmetric directions. A significant difference was also identified during pre-rotation in the transverse plane, where the velocity and acceleration decreased while the jerk increased with increasing asymmetry. The acceleration during trunk flexion was significantly higher than that during extension, in contrast to the velocity, which was higher in extension. A Linear Discriminant Analysis, utilized for classification purposes, demonstrated that 51% of the total performance classifying the three STarT subgroups (65% for high risk) occurred at a position of 15° of rotation to the right during extension. Greater discrimination (67%) was obtained in the classification of the high risk vs. low-medium risk. This study provided a smart "sensor-based" practical methodology for quantitatively assessing and classifying NSLBP patients in clinical settings. The outcomes may also be utilized by leveraging cost-effective inertial sensors, already available in today's smartphones, as objective tools for various health applications towards personalized precision medicine.
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Affiliation(s)
- Mehrdad Davoudi
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Seyyed Mohammadreza Shokouhyan
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Mohsen Abedi
- Physiotherapy Research Center, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran 1616913111, Iran;
| | - Narges Meftahi
- Physical Therapy Department, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz 7194733669, Iran;
- Rehabilitation Sciences Research Center, Shiraz University of Medical Sciences, Shiraz 7194733669, Iran
| | - Atefeh Rahimi
- Department of Physical Therapy, University of Social Welfare and Rehabilitation Sciences, Tehran 1985713871, Iran;
| | - Ehsan Rashedi
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA;
| | - Maryam Hoviattalab
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Roya Narimani
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Mohamad Parnianpour
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Kinda Khalaf
- Department of Biomedical Engineering and Health Engineering Innovation Center, Khalifa University of Science and Technology, P.O. Box 127788 Abu Dhabi, UAE
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Lapinski M, Brum Medeiros C, Moxley Scarborough D, Berkson E, Gill TJ, Kepple T, Paradiso JA. A Wide-Range, Wireless Wearable Inertial Motion Sensing System for Capturing Fast Athletic Biomechanics in Overhead Pitching. Sensors (Basel) 2019; 19:E3637. [PMID: 31438549 DOI: 10.3390/s19173637] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 08/09/2019] [Accepted: 08/15/2019] [Indexed: 11/17/2022]
Abstract
The standard technology used to capture motion for biomechanical analysis in sports has employed marker-based optical systems. While these systems are excellent at providing positional information, they suffer from a limited ability to accurately provide fundamental quantities such as velocity and acceleration (hence forces and torques) during high-speed motion typical of many sports. Conventional optical systems require considerable setup time, can exhibit sensitivity to extraneous light, and generally sample too slowly to accurately capture extreme bursts of athletic activity. In recent years, wireless wearable sensors have begun to penetrate devices used in sports performance assessment, offering potential solutions to these limitations. This article, after determining pressing problems in sports that such sensors could solve and surveying the state-of-the-art in wearable motion capture for sports, presents a wearable dual-range inertial and magnetic sensor platform that we developed to enable an end-to-end investigation of high-level, very wide dynamic-range biomechanical parameters. We tested our system on collegiate and elite baseball pitchers, and have derived and measured metrics to glean insight into performance-relevant motion. As this was, we believe, the first ultra-wide-range wireless multipoint and multimodal inertial and magnetic sensor array to be used on elite baseball pitchers, we trace its development, present some of our results, and discuss limitations in accuracy from factors such as soft-tissue artifacts encountered with extreme motion. In addition, we discuss new metric opportunities brought by our systems that may be relevant for the assessment of micro-trauma in baseball.
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Abstract
We examined falling risk among elderly using a wearable inertial sensor, which combines accelerometer and gyrosensors devices, applied during the Timed Up and Go (TUG) test. Subjects were categorised into two groups as low fall risk and high fall risk with 13.5 s duration taken to complete the TUG test as the threshold between them. One sensor was attached at the subject's waist dorsally, while acceleration and gyrosensor signals in three directions were extracted during the test. The analysis was carried out in phases: sit-bend, bend-stand, walking, turning, stand-bend and bend-sit. Comparisons between the two groups showed that time parameters along with root mean square (RMS) value, amplitude and other parameters could reveal the activities in each phase. Classification using RMS value of angular velocity parameters for sit-stand phase, RMS value of acceleration for walking phase and amplitude of angular velocity signal for turning phase along with time parameters suggests that this is an improved method in evaluating fall risk, which promises benefits in terms of improvement of elderly quality of life.
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Affiliation(s)
- Nor Aini Zakaria
- a Biomedical Imaging and Informatics Department, Nara Institute of Science and Technology , Nara , Japan
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