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Elsler LG, Oostdijk M, Gephart JA, Free CM, Zhao J, Tekwa E, Bochniewicz EM, Giron-Nava A, Johnson AF. Global trade network patterns are coupled to fisheries sustainability. PNAS Nexus 2023; 2:pgad301. [PMID: 37817775 PMCID: PMC10560747 DOI: 10.1093/pnasnexus/pgad301] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 07/12/2023] [Accepted: 08/31/2023] [Indexed: 10/12/2023]
Abstract
The rapid development of seafood trade networks alongside the decline in biomass of many marine populations raises important questions about the role of global trade in fisheries sustainability. Mounting empirical and theoretical evidence shows the importance of trade development on commercially exploited species. However, there is limited understanding of how the development of trade networks, such as differences in connectivity and duration, affects fisheries sustainability. In a global analysis of over 400,000 bilateral trade flows and stock status estimates for 876 exploited fish and marine invertebrates from 223 territories, we reveal patterns between seafood trade network indicators and fisheries sustainability using a dynamic panel regression analysis. We found that fragmented networks with strong connectivity within a group of countries and weaker links between those groups (modularity) are associated with higher relative biomass. From 1995 to 2015, modularity fluctuated, and the number of trade connections (degree) increased. Unlike previous studies, we found no relationship between the number or duration of trade connections and fisheries sustainability. Our results highlight the need to jointly investigate fisheries and trade. Improved coordination and partnerships between fisheries authorities and trade organizations present opportunities to foster more sustainable fisheries.
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Affiliation(s)
- Laura G Elsler
- Stockholm Resilience Centre, Stockholm University, 11419 Stockholm, Sweden
| | - Maartje Oostdijk
- School of Environment and Natural Resources, University of Iceland, 101 Reykjavik, Iceland
| | - Jessica A Gephart
- Department of Environmental Science, American University, Washington, DC 20016, USA
| | - Christopher M Free
- Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
- Marine Science Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Junfu Zhao
- Institute of Marxism, Fudan University, Shanghai 200433, China
| | - Eden Tekwa
- Department of Biology, McGill University, Montreal, QC H3A 1B1, Canada
| | | | - Alfredo Giron-Nava
- Stanford Center for Ocean Solutions, Stanford University, Palo Alto, CA 94305, USA
| | - Andrew F Johnson
- Marine SPACE group, The Lyell Centre, Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, Currie, Scotland EH14 4AS, UK
- MarFishEco Fisheries Consultants Ltd., Edinburgh, Scotland EH7 5HT, UK
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Bochniewicz EM, Emmer G, Dromerick AW, Barth J, Lum PS. Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning. Sensors (Basel) 2023; 23:3111. [PMID: 36991822 PMCID: PMC10058354 DOI: 10.3390/s23063111] [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] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/11/2023] [Accepted: 03/12/2023] [Indexed: 06/19/2023]
Abstract
Trials for therapies after an upper limb amputation (ULA) require a focus on the real-world use of the upper limb prosthesis. In this paper, we extend a novel method for identifying upper extremity functional and nonfunctional use to a new patient population: upper limb amputees. We videotaped five amputees and 10 controls performing a series of minimally structured activities while wearing sensors on both wrists that measured linear acceleration and angular velocity. The video data was annotated to provide ground truth for annotating the sensor data. Two different analysis methods were used: one that used fixed-size data chunks to create features to train a Random Forest classifier and one that used variable-size data chunks. For the amputees, the fixed-size data chunk method yielded good results, with 82.7% median accuracy (range of 79.3-85.8) on the 10-fold cross-validation intra-subject test and 69.8% in the leave-one-out inter-subject test (range of 61.4-72.8). The variable-size data method did not improve classifier accuracy compared to the fixed-size method. Our method shows promise for inexpensive and objective quantification of functional upper extremity (UE) use in amputees and furthers the case for use of this method in assessing the impact of UE rehabilitative treatments.
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Affiliation(s)
- Elaine M. Bochniewicz
- The MITRE Corporation, McLean, VA 22102, USA
- Department of Biomedical Engineering, Catholic University of America, Washington, DC 20064, USA
| | - Geoff Emmer
- The MITRE Corporation, McLean, VA 22102, USA
| | - Alexander W. Dromerick
- Medstar National Rehabilitation Network, Washington, DC 20010, USA
- Veterans Affairs Medical Center, Providence, RI 02908, USA
- Department of Rehabilitation Medicine, Georgetown University, Washington, DC 20057, USA
| | - Jessica Barth
- Medstar National Rehabilitation Network, Washington, DC 20010, USA
- Veterans Affairs Medical Center, Providence, RI 02908, USA
| | - Peter S. Lum
- Department of Biomedical Engineering, Catholic University of America, Washington, DC 20064, USA
- Medstar National Rehabilitation Network, Washington, DC 20010, USA
- Veterans Affairs Medical Center, Providence, RI 02908, USA
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Lum PS, Shu L, Bochniewicz EM, Tran T, Chang LC, Barth J, Dromerick AW. Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method. Neurorehabil Neural Repair 2020; 34:1078-1087. [PMID: 33150830 PMCID: PMC7704838 DOI: 10.1177/1545968320962483] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
BACKGROUND Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results. OBJECTIVE Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity. METHODS Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb. RESULTS The counts ratio was not significantly correlated with ground truth and had large errors (r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 (P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 (P = .005; average error = 5.2%) with ground truth. CONCLUSIONS In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.
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Affiliation(s)
- Peter S Lum
- The Catholic University of America, Washington, DC, USA.,MedStar National Rehabilitation Network, Washington, DC, USA
| | - Liqi Shu
- Warren Alpert Medical School of Brown University, Providence, RI, USA
| | | | - Tan Tran
- The Catholic University of America, Washington, DC, USA
| | | | - Jessica Barth
- MedStar National Rehabilitation Network, Washington, DC, USA
| | - Alexander W Dromerick
- MedStar National Rehabilitation Network, Washington, DC, USA.,Georgetown University School of Medicine, Washington, DC, USA
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Bochniewicz EM, Emmer G, McLeod A, Barth J, Dromerick AW, Lum P. Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning. J Stroke Cerebrovasc Dis 2017; 26:2880-2887. [PMID: 28781056 DOI: 10.1016/j.jstrokecerebrovasdis.2017.07.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Revised: 06/19/2017] [Accepted: 07/10/2017] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND AND PURPOSE Trials of restorative therapies after stroke and clinical rehabilitation require relevant and objective efficacy end points; real-world upper extremity (UE) functional use is an attractive candidate. We present a novel, inexpensive, and feasible method for separating UE functional use from nonfunctional movement after stroke using a single wrist-worn accelerometer. METHODS Ten controls and 10 individuals with stroke performed a series of minimally structured activities while simultaneously being videotaped and wearing a sensor on each wrist that captured the linear acceleration and angular velocity of their UEs. Video data provided ground truth to annotate sensor data as functional or nonfunctional limb use. Using the annotated sensor data, we trained a machine learning tool, a Random Forest model. We then assessed the accuracy of that classification. RESULTS In intrasubject test trials, our method correctly classified sensor data with an average of 94.80% in controls and 88.38% in stroke subjects. In leave-one-out intersubject testing and training, correct classification averaged 91.53% for controls and 70.18% in stroke subjects. CONCLUSIONS Our method shows promise for inexpensive and objective quantification of functional UE use in hemiparesis, and for assessing the impact of UE treatments. Training a classifier on raw sensor data is feasible, and determination of whether patients functionally use their UE can thus be done remotely. For the restorative treatment trial setting, an intrasubject test/train approach would be especially accurate. This method presents a potentially precise, cost-effective, and objective measurement of UE use outside the clinical or laboratory environment.
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Affiliation(s)
- Elaine M Bochniewicz
- The MITRE Corporation, McLean, Virginia; Department of Biomedical Engineering, Catholic University of America, Washington, District of Columbia.
| | | | | | - Jessica Barth
- Medstar National Rehabilitation Network, Washington, District of Columbia
| | - Alexander W Dromerick
- Medstar National Rehabilitation Network, Washington, District of Columbia; Washington DC Veterans Affairs Medical Center, Washington, District of Columbia; Center for Brain Plasticity and Recovery, Georgetown University, Washington, District of Columbia; Department of Rehabilitation Medicine, Georgetown University, Washington, District of Columbia; Department of Neurology, Georgetown University, Washington, District of Columbia
| | - Peter Lum
- Department of Biomedical Engineering, Catholic University of America, Washington, District of Columbia; Medstar National Rehabilitation Network, Washington, District of Columbia; Washington DC Veterans Affairs Medical Center, Washington, District of Columbia; Center for Brain Plasticity and Recovery, Georgetown University, Washington, District of Columbia
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McLeod A, Bochniewicz EM, Lum PS, Holley RJ, Emmer G, Dromerick AW. Using Wearable Sensors and Machine Learning Models to Separate Functional Upper Extremity Use From Walking-Associated Arm Movements. Arch Phys Med Rehabil 2015; 97:224-31. [PMID: 26435302 DOI: 10.1016/j.apmr.2015.08.435] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 07/30/2015] [Accepted: 08/30/2015] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To improve measurement of upper extremity (UE) use in the community by evaluating the feasibility of using body-worn sensor data and machine learning models to distinguish productive prehensile and bimanual UE activity use from extraneous movements associated with walking. DESIGN Comparison of machine learning classification models with criterion standard of manually scored videos of performance in UE prosthesis users. SETTING Rehabilitation hospital training apartment. PARTICIPANTS Convenience sample of UE prosthesis users (n=5) and controls (n=13) similar in age and hand dominance (N=18). INTERVENTIONS Participants were filmed executing a series of functional activities; a trained observer annotated each frame to indicate either UE movement directed at functional activity or walking. Synchronized data from an inertial sensor attached to the dominant wrist were similarly classified as indicating either a functional use or walking. These data were used to train 3 classification models to predict the functional versus walking state given the associated sensor information. Models were trained over 4 trials: on UE amputees and controls and both within subject and across subject. Model performance was also examined with and without preprocessing (centering) in the across-subject trials. MAIN OUTCOME MEASURE Percent correct classification. RESULTS With the exception of the amputee/across-subject trial, at least 1 model classified >95% of test data correctly for all trial types. The top performer in the amputee/across-subject trial classified 85% of test examples correctly. CONCLUSIONS We have demonstrated that computationally lightweight classification models can use inertial data collected from wrist-worn sensors to reliably distinguish prosthetic UE movements during functional use from walking-associated movement. This approach has promise in objectively measuring real-world UE use of prosthetic limbs and may be helpful in clinical trials and in measuring response to treatment of other UE pathologies.
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Affiliation(s)
| | - Elaine M Bochniewicz
- MITRE Corporation, McLean, VA; Department of Biomedical Engineering, Catholic University of America, Washington, DC
| | - Peter S Lum
- Department of Biomedical Engineering, Catholic University of America, Washington, DC; MedStar National Rehabilitation Network, Washington, DC; Washington DC Veterans Affairs Medical Center, Washington, DC; Center for Brain Plasticity and Recovery, Georgetown University, Washington, DC
| | | | | | - Alexander W Dromerick
- MedStar National Rehabilitation Network, Washington, DC; Washington DC Veterans Affairs Medical Center, Washington, DC; Center for Brain Plasticity and Recovery, Georgetown University, Washington, DC; Department of Rehabilitation Medicine, Georgetown University, Washington, DC; Department of Neurology, Georgetown University, Washington, DC.
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