research paper on geological time scale

Geologic Time Scale

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• Felix M. Gradstein 5  

Part of the book series: Encyclopedia of Earth Sciences Series ((EESS))

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Introduction

The geologic time scale (GTS) is the principal tool for deciphering and understanding the long and complex history of our planet, Earth. As Arthur Holmes, the father of the geologic time scale, once wrote (Holmes, 1965 , p. 148): “To place all the scattered pages of earth history in their proper chronological order is by no means an easy task.” Ordering these scattered and torn pages and understanding the physical, chemical, and biological processes that acted on them since Earth appeared and solidified require a detailed and accurate time scale.

This calibration to linear time of the succession of events recorded in the rocks on Earth has three components (Figure 1 ):

The construction of a geologic time scale is the merger of a chronometric scale, measured in years, and a chronostratigraphic scale, consisting of formalized definitions of geologic stages, biostratigraphic zonation units, magnetic polarity zones, and other subdivisions of the...

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Bibliography

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Schmitz, M. D., 2012. Radiogenic isotopes geochronology. In Gradstein, R. M. (ed.), The Geologic Time Scale 2012 . Amsterdam: Elsevier, pp. 115–127.

Vanden Berghe, N., Hilgen, F. J., and Speijer, R. P., 2012. The Paleogene Period. In Gradstein, R. M., et al. (eds.), The Geologic Time Scale 2012 . Amsterdam: Elsevier, pp. 855–923.

Van Kranendonk, M., 2012. A chronostratigraphic division of the Precambrian. In Gradstein, R. M., et al. (eds.), The Geologic Time Scale 2012 . Amsterdam: Elsevier, pp. 299–393.

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Gradstein, F.M. (2016). Geologic Time Scale. In: Harff, J., Meschede, M., Petersen, S., Thiede, J. (eds) Encyclopedia of Marine Geosciences. Encyclopedia of Earth Sciences Series. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6238-1_199

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On The Geologic Time Scale

2012, Newsletters on Stratigraphy

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Geological Society of America Bulletin

Loren Babcock

aditya chowdhary

, the Father of the Geologic Time Scale once wrote (Holmes 1965, p. 148): " To place all the scattered pages of earth history in their proper chronological order is by no means an easy task ". Ordering these scattered and torn pages requires a detailed and accurate time scale. This will greatly facilitate our understanding of the physical, chemical and biological processes since Earth appeared and solidified. Calibration to linear time of the succession of events recorded in the rocks on Earth has three components: Abstract. This report summarizes the international divisions and ages in the Geologic Time Scale, published in 2012 (GTS2012). Since 2004, when GTS2004 was detailed, major developments have taken place that directly bear and have considerable impact on the intricate science of geologic time scaling. Precam brian now has a detailed proposal for chronostratigraphic subdivision instead of an outdated and abstract chrono-metric one. Of 100 chronostratigraphic units in the Phanerozoic 63 now have formal definitions, but stable chronostratigraphy in part of upper Paleozoic, Triassic and Middle Jurassic/Lower Cretaceous is still wanting. Detailed age calibration now exist between radiometric methods and orbital tuning, making 40 Ar-39 Ar dates 0.64 % older and more accurate. In general, numeric uncertainty in the time scale, although complex and not entirely amenable to objective analysis, is improved and reduced. Bases of Paleozoic, Mesozoic and Ceno-zoic are bracketed by analytically precise ages, respectively 541 0.63, 252.16 0.5, and 65.95 0.05 Ma. High-resolution, direct age-dates now exist for base-Carboniferous, base-Permian, base-Jurassic, base-Ceno-manian and base-Eocene. Relative to GTS2004, 26 of 100 time scale boundaries have changed age, of which 14 have changed more than 4 Ma, and 4 (in Middle to Late Triassic) between 6 and 12 Ma. There is much higher stratigraphic resolution in Late Carboniferous, Jurassic, Cretaceous and Paleogene, and improved integration with stable isotopes stratigraphy. Cenozoic and Cretaceous have a refined magneto-biochronology. The spectacular outcrop sections for the Rosello Composite in Sicily, Italy and at Zumaia, Basque Province, Spain encompass the Global Boundary Stratotype Sections and Points for two Pliocene and two Paleocene stages. Since the cycle record indicates, to the best of our knowledge that the stages sediment fill is strati-graphically complete, these sections also may fulfill the important role of stage unit stratotypes for three of these stages, Piacenzian, Zanclean and Danian.

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This chapter will summarize the historical development of geology and in particular the development of the modern uniformitarian view of the geological record and the millions-of-years time-scale. The important proponents of old-earth thinking (including Werner, Smith, Hutton, Cuvier, and Lyell) along with some of the opponents of this view as it was developing (the scriptural geologists) will be discussed, as well as the key ideas in the development of the “geological column”. This historical context is useful in assessing both the current evolution-creation controversy and the debate among creationists about the validity and role of the “geological column” in their models.1

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It has been recommended that geological time be described in a single set of terms and according to metric or SI ("Systeme International d'Unites") standards, to ensure "worldwide unification of measurement". While any effort to improve communication in sci- entific research and writing is to be encouraged, we are also concerned that fundamental differences between date and duration, in the way that our profession expresses geological time, would be lost in such an oversimplified terminology. In addition, no precise value for 'year' in the SI base unit of second has been accepted by the international bodies. Under any circumstances, however, it remains the fact that geologi- cal dates - as points in time - are not relevant to the SI. Known dates may define durations, just as known durations may define dates, or dates may simply be punctual references that support historical narratives, but dates are not quantities. Furthermore, dates, as datum points,...

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Contributions to the Geologic Time Scale

Containing papers given at the Geological Time Scale Symposium in 1976, this volume begins with a review of dating and correlation, and includes papers on the topics of: geochronoloic scales, biochronology, the magnetic polarity time scale, the potassium-argon isotopic dating method, isotopic methods, and worldwide Permian chronostratigraphy, among others.

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Contributions to the Geologic Time Scale Author(s): George V. Cohee, Martin F. Glaessner, Hollis D. Hedberg https://doi.org/10.1306/St6398 ISBN-10: 0891810102 ISBN (electronic): 9781629812007 Publisher: American Association of Petroleum Geologists Published: 1978

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Table of Contents

• Front Matter Open the PDF Link PDF for Front Matter in another window Add to Citation Manager
• Dating and Correlation, A Review Author(s) D. J. Mclaren D. J. Mclaren Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C1 Abstract Open the PDF Link PDF for Dating and Correlation, A Review in another window Add to Citation Manager
• Geochronologic Scales Author(s) W. B. Harland W. B. Harland Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C2 Abstract Open the PDF Link PDF for Geochronologic Scales in another window Add to Citation Manager
• Stratotypes and an International Geochronologic Scale Author(s) Hollis D. Hedberg Hollis D. Hedberg Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C3 Abstract Open the PDF Link PDF for Stratotypes and an International Geochronologic Scale in another window Add to Citation Manager
• Biochronology Author(s) W. A. Berggren ; W. A. Berggren Search for other works by this author on: GSW Google Scholar J. A. van Couvering J. A. van Couvering Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C4 Abstract Open the PDF Link PDF for Biochronology in another window Add to Citation Manager
• The Magnetic Polarity Time Scale: Prospects and Possibilities in Magnetostratigraphy Author(s) M. W. Mcelhinny M. W. Mcelhinny Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C5 Abstract Open the PDF Link PDF for The Magnetic Polarity Time Scale: Prospects and Possibilities in Magnetostratigraphy in another window Add to Citation Manager
• Subcommission on Geochronology: Convention on the Use of Decay Constants in Geochronology and Cosmochronology Author(s) R. H. Steiger ; R. H. Steiger Search for other works by this author on: GSW Google Scholar E. Jäger E. Jäger Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C6 Abstract Open the PDF Link PDF for Subcommission on Geochronology: Convention on the Use of Decay Constants in Geochronology and Cosmochronology in another window Add to Citation Manager
• Pre-Cenozoic Phanerozoic Time Scale—Computer File of Critical Dates and Consequences of New and In-Progress Decay-Constant Revisions Author(s) Richard Lee Armstrong Richard Lee Armstrong Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C7 Abstract Open the PDF Link PDF for Pre-Cenozoic Phanerozoic Time Scale—Computer File of Critical Dates and Consequences of New and In-Progress Decay-Constant Revisions in another window Add to Citation Manager
• Applicability of the Rubidium-Strontium Method to Shales and Related Rocks Author(s) Umberto G. Cordani ; Umberto G. Cordani Search for other works by this author on: GSW Google Scholar Koji Kawashita ; Koji Kawashita Search for other works by this author on: GSW Google Scholar Antonio Thomaz Filho Antonio Thomaz Filho Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C8 Abstract Open the PDF Link PDF for Applicability of the Rubidium-Strontium Method to Shales and Related Rocks in another window Add to Citation Manager
• Potassium-Argon Isotopic Dating Method and Its Application to Physical Time-Scale Studies Author(s) Ian McDougall Ian McDougall Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C9 Abstract Open the PDF Link PDF for Potassium-Argon Isotopic Dating Method and Its Application to Physical Time-Scale Studies in another window Add to Citation Manager
• Results of Dating Cretaceous, Paleogene Sediments, Europe Author(s) G. S. Odin G. S. Odin Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C10 Abstract Open the PDF Link PDF for Results of Dating Cretaceous, Paleogene Sediments, Europe in another window Add to Citation Manager
• Isotopic Ages and Stratigraphic Control of Mesozoic Igneous Rocks in Japan 1 Author(s) Ken Shibata ; Ken Shibata Search for other works by this author on: GSW Google Scholar Tatsuro Matsumoto ; Tatsuro Matsumoto Search for other works by this author on: GSW Google Scholar Takeru Yanagi ; Takeru Yanagi Search for other works by this author on: GSW Google Scholar Reiko Hamamoto Reiko Hamamoto Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C11 Abstract Open the PDF Link PDF for Isotopic Ages and Stratigraphic Control of Mesozoic Igneous Rocks in Japan^{<a href="javascript:;" reveal-id="ch11fn1" data-open="ch11fn1" class="link link-ref link-reveal xref-fn js-xref-fn split-view-modal">¹</a>} in another window Add to Citation Manager
• Isotopic Methods in Quaternary Geology 1 Author(s) Vladimir Šibrava Vladimir Šibrava Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C12 Abstract Open the PDF Link PDF for Isotopic Methods in Quaternary Geology^{<a href="javascript:;" reveal-id="ch12fn1" data-open="ch12fn1" class="link link-ref link-reveal xref-fn js-xref-fn split-view-modal">¹</a>} in another window Add to Citation Manager
• Status of the Boundary between Pliocene and Pleistocene Author(s) K. V. Nikiforova K. V. Nikiforova Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C13 Abstract Open the PDF Link PDF for Status of the Boundary between Pliocene and Pleistocene in another window Add to Citation Manager
• A Radiometric Time Scale for the Neogene of the Paratethys Region Author(s) Dionyz Vass ; Dionyz Vass Search for other works by this author on: GSW Google Scholar Gevorg P. Bagdasarjan Gevorg P. Bagdasarjan Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C14 Abstract Open the PDF Link PDF for A Radiometric Time Scale for the Neogene of the Paratethys Region in another window Add to Citation Manager
• On Dating of the Paleogene Author(s) M. Rubinstein ; M. Rubinstein Search for other works by this author on: GSW Google Scholar L. Gabunia L. Gabunia Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C15 Abstract Open the PDF Link PDF for On Dating of the Paleogene in another window Add to Citation Manager
• A New Paleogene Numerical Time Scale Author(s) J. Hardenbol ; J. Hardenbol Search for other works by this author on: GSW Google Scholar W. A. Berggren W. A. Berggren Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C16 Abstract Open the PDF Link PDF for A New Paleogene Numerical Time Scale in another window Add to Citation Manager
• Critical Review of Isotopic Dates in Relation to Paleogene Stratotypes Author(s) Charles Pomerol Charles Pomerol Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C17 Abstract Open the PDF Link PDF for Critical Review of Isotopic Dates in Relation to Paleogene Stratotypes in another window Add to Citation Manager
• Isotopic Dates for a Paleogene Time Scale Author(s) G. S. Odin G. S. Odin Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C18 Abstract Open the PDF Link PDF for Isotopic Dates for a Paleogene Time Scale in another window Add to Citation Manager
• Cretaceous Time Scale from North America Author(s) Marvin A. Lanphere ; Marvin A. Lanphere Search for other works by this author on: GSW Google Scholar David L. Jones David L. Jones Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C19 Abstract Open the PDF Link PDF for Cretaceous Time Scale from North America in another window Add to Citation Manager
• A Cretaceous Time Scale Author(s) J. E. van Hinte J. E. van Hinte Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C20 Abstract Open the PDF Link PDF for A Cretaceous Time Scale in another window Add to Citation Manager
• A Jurassic Time Scale Author(s) J. E. van Hinte J. E. van Hinte Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C21 Abstract Open the PDF Link PDF for A Jurassic Time Scale in another window Add to Citation Manager
• Chronostratigraphy for the World Permian Author(s) J. B. Waterhouse J. B. Waterhouse Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C22 Abstract Open the PDF Link PDF for Chronostratigraphy for the World Permian in another window Add to Citation Manager
• Report on Isotopic Dating of Rocks in the Carboniferous System Author(s) A. Bouroz A. Bouroz Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C23 Abstract Open the PDF Link PDF for Report on Isotopic Dating of Rocks in the Carboniferous System in another window Add to Citation Manager
• The Mississippian-Pennsylvanian Boundary Author(s) Mackenzie Gordon, Jr. ; Mackenzie Gordon, Jr. Search for other works by this author on: GSW Google Scholar B. L. Mamet B. L. Mamet Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C24 Abstract Open the PDF Link PDF for The Mississippian-Pennsylvanian Boundary in another window Add to Citation Manager
• Devonian Author(s) Willi Ziegler Willi Ziegler Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C25 Abstract Open the PDF Link PDF for Devonian in another window Add to Citation Manager
• The Silurian System Author(s) Nils Spjeldnaes Nils Spjeldnaes Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C26 Abstract Open the PDF Link PDF for The Silurian System in another window Add to Citation Manager
• Ordovician Geochronology Author(s) Reuben J. Ross, Jr. ; Reuben J. Ross, Jr. Search for other works by this author on: GSW Google Scholar Charles W. Naeser ; Charles W. Naeser Search for other works by this author on: GSW Google Scholar Richard S. Lambert Richard S. Lambert Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C27 Abstract Open the PDF Link PDF for Ordovician Geochronology in another window Add to Citation Manager
• The Cambrian System Author(s) J. W. Cowie ; J. W. Cowie Search for other works by this author on: GSW Google Scholar S. J. Cribb S. J. Cribb Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C28 Abstract Open the PDF Link PDF for The Cambrian System in another window Add to Citation Manager
• Numerical Correlation of Middle and Upper Precambrian Sediments Author(s) Michel G. Bonhomme Michel G. Bonhomme Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C29 Abstract Open the PDF Link PDF for Numerical Correlation of Middle and Upper Precambrian Sediments in another window Add to Citation Manager
• Aspects of the Revised South African Stratigraphic Classification and a Proposal for the Chronostratigraphic Subdivision of the Precambrian Author(s) L. E. Kent ; L. E. Kent Search for other works by this author on: GSW Google Scholar P. J. Hugo P. J. Hugo Search for other works by this author on: GSW Google Scholar Doi: https://doi.org/10.1306/St6398C30 Abstract Open the PDF Link PDF for Aspects of the Revised South African Stratigraphic Classification and a Proposal for the Chronostratigraphic Subdivision of the Precambrian in another window Add to Citation Manager
• Back Matter Open the PDF Link PDF for Back Matter in another window Add to Citation Manager
• chronostratigraphy
• geochronology
• stratigraphy
• time scales

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Published on 8.5.2024 in Vol 26 (2024)

Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study

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• Christian Wolff 1 , MSc   ; 
• Patrick Steinheimer 2 , MSc   ; 
• Elke Warmerdam 3 , MSc, PhD   ; 
• Tim Dahmen 1 , MSc, PhD   ; 
• Philipp Slusallek 1 , MSc, PhD   ; 
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1 German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany

2 Department of Trauma, Hand and Reconstructive Surgery, Departments and Institutes of Surgery, Saarland University, Homburg/Saar, Germany

3 Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Homburg/Saar, Germany

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Background: Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways.

Objective: For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized.

Methods: In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: −20%, −15%, −10%, −5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests.

Results: Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P <.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking.

Conclusions: While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.

Introduction

Long-term monitoring of gait patterns and plantar-pressure distributions via insoles are increasingly popular ways to study behavior and activity in the field and in the everyday lives of people and patients, including healing, personalized prevention, and treatment or disease progression [ 1 - 3 ]. In recent years, the usability of instrumented insoles for gait analyses has increased. Several technical issues could be resolved, including calibration, hysteresis and drift, durability, usability, limited energy supply and battery life, data storage capacity, and the restriction to low sample frequencies associated with higher error rates, that is, when force peaks are missed [ 3 - 5 ]. The usability of instrumented insoles is currently still limited by difficulties in data analysis. Advanced algorithms and tools are needed and currently developed to be able to draw meaningful conclusions from such insole gait data [ 6 , 7 ]. When analyzing long-term field data and developing smart health care innovations, automated data annotation is desirable to determine and quantify the activities a person has conducted. Ideally, the activity type can be determined algorithmically from the plantar pressure data alone.

Characteristic gait changes have been reported for walking on slopes, such as changes in the contribution of the ankle joint to leg work [ 8 ]. In addition, uphill walking on a treadmill increases hip and knee flexion angles during the stance phase, as well as the forward tilt of the thorax [ 9 ]. Furthermore, a decrease in dorsiflexion was observed during downhill walking at initial contact, in midstance, and during the second half of the swing phase [ 9 ]. During uphill walking with increasing inclination, more positive joint work was identified for the ankle and hip joint, while negative joint work increased during downhill walking [ 10 ]. Older individuals were shown to have a disproportionate recruitment of hip muscles and smaller increases in activity of the medial gastrocnemius muscle with steeper uphill slopes than younger adults, resulting in difficulty walking on steep slopes [ 11 ].

The M-shaped curve of ground reaction forces or plantar pressure during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods [ 12 ]. When monitoring gait continuously via insoles, walking uphill or downhill on a slope could affect the gait cycle curve in characteristic ways. If these typical changes were known, one could correct for such confounders when analyzing insole data. We hypothesized that walking on a slope generates typical changes in the plantar pressure stance phase curve that vary between uphill and downhill walking.

Study Design

This study is part of the project Smart Implants 2.0—Weight-bearing and Gait Observation for Early Monitoring of Fracture Healing and Individualized Therapy after Trauma, funded by the Werner Siemens Foundation. It was registered in the German Clinical Trials Register (DRKS00025108).

Ethical Considerations

Ethical approval was obtained from the Institutional Review Board of Saarland Medical Board (Ärztekammer des Saarlandes, 30/21).

Data Collection

Inclusion criteria were the ability to walk on a treadmill, and aged 18 years and older. Exclusion criteria were aged under 18 years, use of walking aids, inability to give consent, pregnancy, immobility, and previous injury of the lower legs or pelvis. The aim was to collect data from healthy volunteers.

The healthy participants of both sexes (none of them identified as diverse) were fitted with individually calibrated OpenGO insoles (Moticon GmbH) with 16 pressure sensors in each insole to be used in regular running shoes. Calibration to the individual body weight was conducted using the Moticon OpenGO app by letting the participants walk and shift their body weight in a standardized way. The insole size was selected to fit the individual participant’s shoe size. Measurements were conducted with a recording frequency of 100 Hz in the record mode of the device. Raw data were exported for analyses. The participants walked on a treadmill at 4 km/h (Mercury, HP Cosmos) for 1 minute while insole data were collected with 3-minute breaks for recovery. Recordings were obtained for slopes of −20%, −15%, −10%, −5%, 0%, 5%, 10%, 15%, and 20%. The participants were asked to walk for 1 minute straight, and recording was only commenced when the walking was already in progress to avoid bias by including altered steps upon gait initiation.

Data Processing

The pressure readings of the force sensors in the insole device yield a weighted sum as a total vertical ground reaction force reading. To compute the force, every summand is weighted by its sensor area and a respective scaling factor accounting for the sensor’s surrounding area, as well as gaps between sensors that depend on the insole size. This process is conducted by the Moticon software as an automated processing step before file export. Insole data were exported as described previously [ 13 , 14 ]. A custom-developed data platform was then used for further processing and parameter calculation, in which step detection was conducted as follows. The stance phases were identified and extracted from the time series data by considering any activity with consecutive force readings above 30 N. A tolerance of up to 3 missing values was implemented to account for possible recording issues. Any activity with a duration of less than 300 milliseconds or more than 2000 milliseconds was discarded. Both the force and time axes were normalized. Force readings were transformed from Newton to a proportion of the body weight of the respective participant. Of note, as plantar pressure was measured instead of weight, due to acceleration, values regularly exceeded the body weight for peak load-bearing instances. Normalizing the time axis was more complex, as the lack of a fixed cadence resulted in varying step lengths and thus differing numbers of true measurement points for each step. Therefore, a natural cubic spline interpolation was conducted on the original raw data. Based on the resulting curve for each stance phase, 100 equidistant samples were taken, resulting in an interpolated force measurement point for every 1% of the overall stance phase length. This approach accounted for the lower recording frequency and higher sensor noise inherent to the insoles when compared with other gait measuring devices, such as sensor-equipped treadmills or force plates. Parameters that describe the trajectory of the stance phase curve are usually based on or derived from the characteristic local extrema, that is, the first and second force peak and the local minimum in-between force peaks. These maxima and the minimum are used as parameters themselves to describe the curve trajectory [ 13 ]. Sensor jitter may lead to the existence of multiple ambiguous candidates for the named extrema. As a solution to this, a Gaussian filter was applied to the original raw data in a repetition of the normalization process. The applied filtering strategy (σ=3, kernel size 7) was chosen to prioritize the elimination of extrema ambiguity at the expense of signal precision. This can result in overcorrection in areas with higher signal volatility, mostly at the start and end of the stance phase. Hence, to avoid loss of high-frequency detail, the filtered and normalized curve was not used for parameter analysis, but only to determine unambiguous time-axis positions (indices) for the extremum candidates. These indices were then reapplied to the nonfiltered, normalized data to identify the corresponding plantar pressure measurement closest to the original raw data. In case the use of the filtered data still led to inconclusive extremum candidates, the following additional detection strategies were applied in the named order: (1) time plausibility: extremum candidates occurring within the first or last 10 indices (first/last 10% of overall time span) were eliminated; (2) maximum or minimum-pool filtering: should multiple extremum candidates occur within a pool size of 5 indices (equals to 5% of overall time span), the candidate with the highest or lowest force value was chosen; (3) monotony-check: in case of multiple remaining extremum candidates, candidates where the curve did not display a strict monotonous decrease or increase in both directions within 5 indices each were eliminated; and (4) monotony grace: in case the monotony check had eliminated too many candidates (less than 2 maximum candidates or less than 1 minimum candidate remaining), the eliminated candidates were reinstated in descending order of their highest achieved monotony distance until the target number of candidates was reached.

After applying these strategies, every stance activity that remained with an irregular amount of unambiguous extremum candidates was removed from the data set. In total, 585 load-bearing events were excluded as not fitting the strict parameter definitions.

For each participant, across the minute of walking all stance phase curves were extracted. The parameters illustrated in Figure 1 were calculated for each stance phase and used to analyze changes in the trajectory of the stance phase curve. To do so, data from both feet were pooled. The curve is mainly described by 2 maxima and a minimum in between the maxima, Fz2 (the first maximum), Fz3 (the minimum), and Fz4 (the second maximum). The mean force over the entire stance phase is referred to as Fmean stance . The mean force between the start of the loading phase and Fz2 is Fmean load . The mean force between Fz2 and Fz4 is Fmean mid . The mean force between Fz4 and the end of the unloading phase is Fmean unload . All these parameters have the unit percent body weight. In addition, the loading and unloading slope have the units percent body weight or percent stance phase duration. The loading slope was computed as the slope of the line defined by the start of the loading phase and the first force reading equal to or higher than 80% of Fz2. The unloading slope was calculated as the slope of the line defined by the first force reading in the unloading phase below 80% of Fz4 and the end of the stance phase event.

Statistical Analyses

Statistical tests were executed with SPSS Statistics (version 29; IBM Corp). Significance was defined as P <.05. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. A linear regression analysis of variance was conducted for each of the gait parameters as the dependent variable, with the slope (−20% to 20%) as the independent variable. Mean values and SD are reported. Linear regression slopes are reported for comparability and to allow for correction, even though for some of the parameters other but differing regression types yielded higher R 2 values. The sample size of 40 was an estimate based on what is common in the field, and taking into account the aim to measure a very diverse group of volunteers. An a priori sample size calculation was not conducted due to a lack of comparable data.

Measurements were taken from 40 healthy participants (19 women and 21 men) with an average age of 43.90 (SD 17.30, range 18-87) years. Participant characteristics are summarized in Table 1 . Data were successfully recorded for all of the participants and slope levels, resulting in a complete data set ( Multimedia Appendix 1 ).

Data were normally distributed. Figure 2 visualizes the differences between the analyzed slope values on the stance phase curve. Figure 3 shows the normalized changes in the analyzed parameters with the slope of the treadmill. The analysis of variance revealed significant changes with the slope for Fmean load , Fmean unload , Fz2, Fz3, Fz4, loading and unloading slope (all P <.001). There was no significant correlation of the slope with Fmean stance ( P =.98) and Fmean mid ( P =.13). Other than the other parameters with significant changes related to slope, Fz3 had its peak at horizontal walking and values dropped when walking uphill and downhill alike. Thus, a simultaneous and short-term increase in loading slope and Fmean load combined with a decrease in Fmean unload , Fz2, Fz4, and the unloading slope indicates downhill walking, while the opposite indicates uphill walking. Fz3 is not a suitable parameter to distinguish between uphill and downhill walking, as its value decreases both when walking uphill as well as downhill. Mean values and the SD of the analyzed parameters for each treadmill slope level in absolute values are displayed in Table 2 . Table 3 indicates the linear regression slopes and R 2 -values for each of the curves shown in Figure 3 .

a Fmean stance : the mean force over the entire stance phase.

b Fmean load : the mean force between the start of the loading phase and Fz2.

c Fmean mid : the mean force between Fz2 and Fz4.

d Fmean unload : the mean force between Fz4 and the end of the unloading phase.

e Fz2: the first maximum.

f Fz3: the minimum.

g Fz4: the second maximum.

Principal Results

This study identified characteristic changes when walking with an uphill or downhill slope in insole plantar pressure data of healthy participants. The most pronounced changes with treadmill slope were found in the loading slope of the curve. A typical combination of changes in several parameters was reported that defines uphill and downhill walking and may be used for annotation and correction when analyzing such data. These changes in the trajectory of the force curve with different surface slopes relative to the force vector of Earth’s gravity are related to changes in plantar load distribution. When walking downhill, Fz2 was found to be higher compared to when walking uphill, which is caused by the more pronounced force transfer through the heel of the foot, followed by a lower second maximum due to the even lower surface at push-off.

While patient-related factors, such as curve characteristics related to body size, muscle power, degenerative disease, etc, would remain constant throughout an insole measurement, fatigue-related changes [ 15 ] may increasingly appear and then stay toward the later stages of a recording of a walking bout. Additionally, age, body height, body weight, BMI, and handgrip strength were shown to cause characteristic changes in the plantar pressure force curve, that would usually only change on a long-term scale [ 16 ]. In contrast, as shown in the present data set, walking on slopes leads to temporary and characteristic changes in specific properties of the stance-phase curve. Changes over time in the identified parameters should thus be considered and correctly interpreted when studying long-term field gait data collected via insoles. To analyze the healing process, that is, after an injury, slow changes in parameters would be expected, and a trend toward what is considered normal over several weeks [ 17 ]. Short-term changes over minutes or hours would thus not be explainable by the healing progress and should have a different cause. In addition, the asymmetry between the legs should slowly decrease throughout healing [ 18 ]. When walking on a slope, asymmetry could also be affected, if the injury causes increasing problems such as pain when walking uphill or downhill. It is also recommendable to identify the characteristics of walking with walking aids, such as crutches, to be able to classify the nature of the observed changes and the treatment stage better.

Limitations

Effects of walking speed were not analyzed in this study, even though it is known that lower extremity joint loading is affected by varying step length and cadence during graded uphill and downhill walking [ 19 ]. These parameters, however, do not seem to be necessary to successfully annotate gait data obtained by insoles. For participant or patient convenience, it would be desirable if insoles did not need to be combined with further devices or wearables. The present data suggest that at least the identification of walking on slopes does not require further sensors. It is also known that kinematic, kinetic, and electromyographic parameters differ between treadmill walking and overground gait, while spatiotemporal, kinematic, kinetic, electromyographic, and energy consumption outcome measures are largely comparable [ 20 ]. Another limitation of this study is that the parameters analyzed here can only be used when a regular gait curve is present. If this is not the case, other methods need to be applied, that is, machine learning for step detection and segmentation or the analysis of further parameters, possibly slopes and averages, or differences between individual sensors [ 21 ]. Differences between the 16 sensors embedded in each insole were not analyzed in this study and could be assessed in the future, for example, to distinguish between ground types (gravel, sand, etc). Another limitation is that the present characteristic changes that were assessed in healthy participants may differ for patients with gait disorders, depending on their disease or injury type. It will therefore be important to collect longitudinal data on different slopes from patients with defined diseases and injuries throughout the healing process or throughout different disease stages. These studies would serve to identify if the reported findings are valid also for patients, and for which patient groups this is true.

Use of Wearables in Patients

The insole technology and present data may be valuable in real-world settings when investigating changes in mechanical properties during walking, that is, in occupational health research, sport and exercise science, for urban planning, and to plan inclusive architecture. For instance, the global average slope of urban areas is about 3.70° [ 22 ]. Wearables such as pressure insoles are increasingly used to study gait and movement, as well as for fall detection, fall classification, and fall risk assessment in the daily life of patients, and furthermore for lifestyle and health monitoring [ 1 , 3 , 23 - 27 ]. Long-term monitoring, especially if combined with additional sensors, may produce large amounts of data that require advanced strategies for analyses. Apart from regression statistics, among the options is the use of machine learning algorithms trained with annotated data for pattern recognition [ 24 , 26 ]. For longer-term monitoring of patients, it would be desirable if such algorithms were trained to identify various key activities of daily life that might indicate the level of healing progress. For example, when a patient with a tibial fracture is capable of cycling again, this is likely an indication for advances in the healing process. It would also be of interest to identify risky behavior, possibly leading to excessive forces, and to warn the patient by giving, for example, an audible or haptic warning signal. To guarantee meaningful data interpretation, machine learning may be combined with conventional regression-based analyses, such as the ones proposed in this paper to best tackle data complexity. Furthermore, prediction algorithms could be implemented for falls and diseases that enable more refined individual recommendations. Ideally, such interventions would be based on live data analyses. Limitations in the computing power of small wearable devices can increasingly be mitigated by both algorithmic optimization techniques in machine learning, such as dimensionality reduction, reservoir computing, and network pruning, as well as hardware innovations [ 27 , 28 ]. In the near future, such advances will likely allow real-time feedback based on data from various sources combined [ 29 , 30 ]. Alternatively, extracting decision-making systems (symbolic artificial intelligence), such as threshold-based methods, might offer an immediate route to real-time feedback.

Sensors in Orthoses and Implants

Apart from insoles, very similar data might be collected from mechanical sensors embedded in orthoses [ 31 ] or implants [ 32 ]. Potentially, walking on a slope in these recordings changes the data in similar ways as described here. It would be highly desirable if patients did not need to use separate wearables such as insoles anymore, but if orthoses and implants had sensors embedded, not only to monitor healing progress but also to identify healing problems or complications and the need for surgical revision [ 33 ]. If similar load data could be collected by sensors in artificial hip or knee joints, or potentially even by plates or nails that stabilize bone fractures, recovery regimen could be monitored continuously and advice given on time [ 34 ]. Alarms could go off if forces exceeded certain thresholds or if live pattern analyses revealed unfavorable patterns known to be associated with exceeding forces or problems. As these developments seem to have a high potential with regard to rehabilitation and postoperative treatment, data analyses of insole data should be further studied and ideally, details such as algorithms and characteristics should be published to enable for the further development and widespread application of the named interventions.

Conclusions

Characteristic changes in the plantar-pressure stance phase curve were identified, which reflect uphill and downhill walking. Automated annotation and continuous analyses of gait data via wearables could enable improved rehabilitation and feedback systems for prevention and treatment. A combination of traditional regression statistics embedded in heuristics combined with artificial intelligence methods may yield the best results.

Acknowledgments

The Werner Siemens Foundation (project Smart Implants 2.0) funded this work. The authors would like to acknowledge the help of Aynur Gökten and Jacqueline Orth during the measurements, as well as the help of Lisa-Marie Jost in designing Figure 1 .

Authors' Contributions

CW contributed to the data processing platform, data analysis, methods, and Figure 2 . P Steinheimer conducted the measurements. BG contributed to the idea; ran the statistical analyses; interpreted the data; made the tables; and drafted, submitted, and revised this paper. TD, CS, and FC took part in the data platform implementation. EW, TD, P Slusallek, CS, FC, MO, and TP helped with data interpretation. All authors have contributed to this paper’s drafting and revision, and read and approved the submitted version of this paper.

Conflicts of Interest

TP is President and Board Member of the AO-Foundation, Switzerland, and Extended Board Member of the German Society of Orthopedic Trauma Surgery (DGU), the German Society of Orthopedic Surgery and Traumatology (DGOU), and the German Society of Surgery (DGCH). TP is also the speaker of the Medical Advisory Board of the German Ministry of Defence. The other authors do not have a conflict of interest.

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Abbreviations

Edited by G Eysenbach, T Leung; submitted 24.01.23; peer-reviewed by M Kraus, S Okita; comments to author 21.12.23; revised version received 11.01.24; accepted 17.02.24; published 08.05.24.

©Christian Wolff, Patrick Steinheimer, Elke Warmerdam, Tim Dahmen, Philipp Slusallek, Christian Schlinkmann, Fei Chen, Marcel Orth, Tim Pohlemann, Bergita Ganse. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.05.2024.

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