Method to Quantify Cadence Variability of Individuals with Lower-Limb Amputation

Arch, Elisa S.; Erol, Ozan; Bortz, Connor; Madden, Chelsea; Galbraith, Matthew; Rossi, Anthony; Lewis, Jessica; Higginson, Jill S.; Buckley, Jenni M.; Horne, John Robert

doi:10.1097/jpo.0000000000000124

Cited by 9 publications

(28 citation statements)

References 27 publications

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“…Sensor locations: A = anatomical, P = prosthesis, L = liner. First author (Date) Title Sample (Details) Sensors (Locations) Activities identified (Duration) Aim Outcomes Developing or validating actimeters, algorithms and/or scores for activity classification Arch (2017) [ 30 ] Method to Quantify Cadence Variability of Individuals with Lower-Limb Amputation 27 participants (10 BK and 1AK at K2, 10 BK and 6 AK at K3) Fitbit One (P. Ankle) Activity level – steps, cadence (7 days) Develop a method of quantifying real-world cadence variability. This method of quantifying cadence variability can differentiate between K2 and K3 groups.…”

Section: Resultsmentioning

confidence: 99%

“…The 13 papers on developing or validating actimeters, algorithms or scores for activity classification for lower-limb prosthesis users mainly focused on development of sensors for monitoring activity [ 34 , 35 , 37 , 39 , 42 ]. However, they also included development of smart-phone software to monitor falls [ 40 ] or visualise gait [ 97 ] and papers on comparing sensors [ 31 , 98 ], validating sensors [ 38 ] and validating classification methods [ 30 ]. In the case of upper-limb studies, three papers related to the development of algorithms for the assessment of activity [ 32 , 33 , 41 ].…”

Section: Resultsmentioning

confidence: 99%

“…Developing and validating actimeters, algorithms or scores for activity classification There were few papers that developed ways to accurately monitor lower-limb prosthesis use with more detail than a simple step-count, and few that collected information on the types of activities being performed. This review shows that step detection methods have been wellestablished and are consistent across actimeters, though less accurate at low walking speeds [30,108].…”

Section: Appraisal Of Studies By Classificationmentioning

confidence: 99%

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Technology for monitoring everyday prosthesis use: a systematic review

Chadwell

Diment

Micó-Amigo

et al. 2020

J NeuroEngineering Rehabil

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Background: Understanding how prostheses are used in everyday life is central to the design, provision and evaluation of prosthetic devices and associated services. This paper reviews the scientific literature on methodologies and technologies that have been used to assess the daily use of both upper-and lower-limb prostheses. It discusses the types of studies that have been undertaken, the technologies used to monitor physical activity, the benefits of monitoring daily living and the barriers to long-term monitoring, with particular focus on low-resource settings. Methods: A systematic literature search was conducted in PubMed, Web of Science, Scopus, CINAHL and EMBASE of studies that monitored the activity of prosthesis users during daily-living. Results: Sixty lower-limb studies and 9 upper-limb studies were identified for inclusion in the review. The first studies in the lower-limb field date from the 1990s and the number has increased steadily since the early 2000s. In contrast, the studies in the upper-limb field have only begun to emerge over the past few years. The early lowerlimb studies focused on the development or validation of actimeters, algorithms and/or scores for activity classification. However, most of the recent lower-limb studies used activity monitoring to compare prosthetic components. The lower-limb studies mainly used step-counts as their only measure of activity, focusing on the amount of activity, not the type and quality of movements. In comparison, the small number of upper-limb studies were fairly evenly spread between development of algorithms, comparison of everyday activity to clinical scores, and comparison of different prosthesis user populations. Most upper-limb papers reported the degree of symmetry in activity levels between the arm with the prosthesis and the intact arm. Conclusions: Activity monitoring technology used in conjunction with clinical scores and user feedback, offers significant insights into how prostheses are used and whether they meet the user's requirements. However, the cost, limited battery-life and lack of availability in many countries mean that using sensors to understand the daily use of prostheses and the types of activity being performed has not yet become a feasible standard clinical practice. This review provides recommendations for the research and clinical communities to advance this area for the benefit of prosthesis users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Appraisal Of Studies By Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Technology for monitoring everyday prosthesis use: a systematic review

Chadwell

Diment

Micó-Amigo

et al. 2020

J NeuroEngineering Rehabil

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“…34,35,37,39,97 However, they also included development of smart-phone software to monitor falls 40 or visualise gait, 98 and papers on comparing sensors, 31,99 validating sensors 38 and validating classification methods. 30 In the case of upper-limb studies, three papers related to the development of algorithms for the assessment of activity. 32,33,41 Two of these papers reported on the use of head mounted cameras for the development of grasp taxonomies.…”

Section: Figure 1 Flow Chart Of Selection and Sorting Methodsmentioning

confidence: 99%

“…This review shows that step detection methods have been well-established and are consistent across actimeters, though less accurate at low walking speeds. 30,109 Step-count can be a useful indicator of exercise, but it does not This is a pre-print of the paper 'Technology for monitoring everyday prosthesis use: a systematic review', by Chadwell, Diment, Mico Amigo, Morgado Ramirez et al…”

Section: Developing and Validating Actimeters Algorithms Or Scores Fmentioning

confidence: 99%

Technology for monitoring everyday prosthesis use: a systematic review

Chadwell¹,

Diment²,

Amigo³

et al. 2020

Preprint

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BackgroundUnderstanding how prostheses are used in everyday life is central to the design, provision and evaluation of prosthetic devices and associated services. This paper reviews the scientific literature on methodologies and technologies that have been used to assess the daily use of both upper- and lower-limb prostheses. It discusses the types of studies that have been undertaken, the technologies used to monitor physical activity, the benefits of monitoring daily living and the barriers to long-term monitoring.MethodsA systematic literature search was conducted in PubMed, Web of Science, Scopus, CINAHL and EMBASE of studies that monitored the activity of prosthesis-users during daily-living.Results60 lower-limb studies and 9 upper-limb studies were identified for inclusion in the review. The first studies in the lower-limb field date from the 1990s and the number has increased steadily since the early 2000s. In contrast, the studies in the upper-limb field have only begun to emerge over the past few years. The early lower-limb studies focused on the development or validation of actimeters, algorithms and/or scores for activity classification. However, most of the recent lower-limb studies used activity monitoring to compare prosthetic components. The lower-limb studies mainly used step-counts as their only measure of activity, focusing on the amount of activity, not the type and quality of movements. In comparison, the small number of upper-limb studies were fairly evenly spread between development of algorithms, comparison of everyday activity to clinical scores, and comparison of different prosthesis user populations. Most upper-limb papers reported the degree of symmetry in activity levels between the arm with the prosthesis and the intact arm.ConclusionsActivity monitoring technology used in conjunction with clinical scores and user feedback, offers significant insights into how prostheses are used and whether they meet the user’s requirements. However, the cost, limited battery-life and lack of availability in many countries mean that using sensors to understand the daily use of prostheses and the types of activity being performed has not yet become a feasible standard clinical practice. This review provides recommendations for the research and clinical communities to advance this area for the benefit of prosthesis users.

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