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Poveda L, Devane K, Lalwala M, Gayzik FS, Stitzel JD, Weaver AA. Injury Risk Predictions in Lunar Terrain Vehicle (LTV) Extravehicular Activities (EVAs): A Pilot Study. Ann Biomed Eng 2024:10.1007/s10439-024-03543-8. [PMID: 38836980 DOI: 10.1007/s10439-024-03543-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 05/08/2024] [Indexed: 06/06/2024]
Abstract
Extravehicular activities will play a crucial role in lunar exploration on upcoming Artemis missions and may involve astronauts operating a lunar terrain vehicle (LTV) in a standing posture. This study assessed kinematic response and injury risks using an active muscle human body model (HBM) restrained in an upright posture on the LTV by simulating dynamic acceleration pulses related to lunar surface irregularities. Linear accelerations and rotational displacements of 5 lunar obstacles (3 craters; 2 rocks) over 5 slope inclinations were applied across 25 simulations. All body injury metrics were below NASA's injury tolerance limits, but compressive forces were highest in the lumbar (250-550N lumbar, tolerance: 5300N) and lower extremity (190-700N tibia, tolerance: 1350N) regions. There was a strong association between the magnitudes of body injury metrics and LTV resultant linear acceleration (ρ = 0.70-0.81). There was substantial upper body motion, with maximum forward excursion reaching 375 mm for the head and 260 mm for the chest. Our findings suggest driving a lunar rover in an upright posture for these scenarios is a low severity impact presenting low body injury risks. Injury metrics increased along the load path, from the lower body (highest metrics) to the upper body (lowest metrics). While upper body injury metrics were low, increased body motion could potentially pose a risk of injury from flail and occupant interaction with the surrounding vehicle, suit, and restraint hardware.
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Affiliation(s)
- Luis Poveda
- Department of Biomedical Engineering, Center for Injury Biomechanics, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
- Virginia Tech-Wake Forest Center for Injury Biomechanics, 575 N. Patterson Ave, Suite 530, Winston-Salem, NC, 27101, USA
| | - Karan Devane
- Department of Biomedical Engineering, Center for Injury Biomechanics, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
- Virginia Tech-Wake Forest Center for Injury Biomechanics, 575 N. Patterson Ave, Suite 530, Winston-Salem, NC, 27101, USA
| | - Mitesh Lalwala
- Department of Biomedical Engineering, Center for Injury Biomechanics, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
- Virginia Tech-Wake Forest Center for Injury Biomechanics, 575 N. Patterson Ave, Suite 530, Winston-Salem, NC, 27101, USA
| | - F Scott Gayzik
- Department of Biomedical Engineering, Center for Injury Biomechanics, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
- Virginia Tech-Wake Forest Center for Injury Biomechanics, 575 N. Patterson Ave, Suite 530, Winston-Salem, NC, 27101, USA
| | - Joel D Stitzel
- Department of Biomedical Engineering, Center for Injury Biomechanics, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
- Virginia Tech-Wake Forest Center for Injury Biomechanics, 575 N. Patterson Ave, Suite 530, Winston-Salem, NC, 27101, USA
| | - Ashley A Weaver
- Department of Biomedical Engineering, Center for Injury Biomechanics, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA.
- Virginia Tech-Wake Forest Center for Injury Biomechanics, 575 N. Patterson Ave, Suite 530, Winston-Salem, NC, 27101, USA.
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Wang L, Song P, Cheng C, Han P, Fu L, Chen X, Yu H, Yu X, Hou L, Zhang Y, Guo Q. The Added Value of Combined Timed Up and Go Test, Walking Speed, and Grip Strength on Predicting Recurrent Falls in Chinese Community-dwelling Elderly. Clin Interv Aging 2021; 16:1801-1812. [PMID: 34675495 PMCID: PMC8502011 DOI: 10.2147/cia.s325930] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/11/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose To determine whether combined performance-based models could exert better predictive values toward discriminating community-dwelling elderly with high risk of any-falls or recurrent-falls. Participants and Methods This prospective cohort study included a total of 875 elderly participants (mean age: 67.10±5.94 years) with 513 females and 362 males, recruited from Hangu suburb area of Tianjin, China. All participants completed comprehensive assessments. Methods We documented information about sociodemographic information, behavioral characteristics and medical conditions. Three functional tests—timed up and go test (TUGT), walking speed (WS), and grip strength (GS) were used to create combined models. New onsets of any-falls and recurrent-falls were ascertained at one-year follow-up appointment. Results In total 200 individuals experienced falls over a one-year period, in which 66 individuals belonged to the recurrent-falls group (33%). According to the receiver operating characteristic curve (ROC), the cutoff points of TUGT, WS, and GS toward recurrent-falls were 10.31 s, 0.9467 m/s and 0.3742 kg/kg respectively. We evaluated good performance as “+” while poor performance as “–”. After multivariate adjustment, we found “TUGT >10.31 s” showed a strong correlation with both any-falls (adjusted odds ratio (OR)=2.025; 95% confidence interval (CI)=1.425–2.877) and recurrent-falls (adjusted OR=2.150; 95%CI=1.169–3.954). Among combined functional models, “TUGT >10.31 s, GS <0.3742 kg/kg, WS >0.9467 m/s” showed strongest correlation with both any-falls (adjusted OR=5.499; 95%CI=2.982–10.140) and recurrent-falls (adjusted OR=8.260; 95%CI=3.880–17.585). And this combined functional model significantly increased discriminating abilities on screening recurrent-fallers than a single test (C-statistics=0.815, 95%CI=0.782–0.884, P<0.001), while not better than a single test in predicting any-fallers (P=0.083). Conclusion Elderly people with poor TUGT performance, weaker GS but quicker WS need to be given high priority toward fall prevention strategies for higher risks and frequencies. Meanwhile, the combined “TUGT–, GS–, WS+” model presents increased discriminating ability and could be used as a conventional tool to discriminate recurrent-fallers in clinical practice.
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Affiliation(s)
- Lu Wang
- Department of Rehabilitation, School of Medical Technology, Tianjin Medical University, Tianjin, People's Republic of China
| | - Peiyu Song
- Department of Rehabilitation, School of Medical Technology, Tianjin Medical University, Tianjin, People's Republic of China
| | - Cheng Cheng
- Department of Rehabilitation, School of Medical Technology, Tianjin Medical University, Tianjin, People's Republic of China.,Department of Rehabilitation, Tianjin Huanhu Hospital, Tianjin, People's Republic of China
| | - Peipei Han
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China
| | - Liyuan Fu
- Department of Rehabilitation, School of Medical Technology, Tianjin Medical University, Tianjin, People's Republic of China
| | - Xiaoyu Chen
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China
| | - Hairui Yu
- Department of Rehabilitation, School of Medical Technology, Tianjin Medical University, Tianjin, People's Republic of China
| | - Xing Yu
- Department of Rehabilitation, School of Medical Technology, Tianjin Medical University, Tianjin, People's Republic of China
| | - Lin Hou
- Department of Rehabilitation, School of Medical Technology, Tianjin Medical University, Tianjin, People's Republic of China
| | - Yuanyuan Zhang
- Department of Rehabilitation, School of Medical Technology, Tianjin Medical University, Tianjin, People's Republic of China
| | - Qi Guo
- Department of Rehabilitation, School of Medical Technology, Tianjin Medical University, Tianjin, People's Republic of China.,College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China
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