Technical note: Sedidetection – deriving the nature of the sediment bottom by analyzing gait data
DOI:
https://doi.org/10.3112/erdkunde.2025.03.10Keywords:
coastal morphology, Schleswig-Holstein, North Sea, Schleswig-Holstein Wadden Sea, sedimentsAbstract
In order to comply with EU guidelines for monitoring the condition of the Wadden Sea, regular transect surveys are required. Those are done by taking point sediment samples to determine the near-surface sediment composition. However, these methods are expensive and time-consuming. Remote sensing analyses provide only limited information of the first millimeters of the surface. The results are influenced by interfering signals. The application of motion analysis based on smartphone sensors was investigated in order to draw conclusions about the sediment composition along transects. The aim was to identify homogeneous sediment areas and their boundaries along the transects, thereby significantly reducing the need for traditional sediment sampling. In addition, it was examined whether qualitative statements on sediment composition can already be made with sufficient accuracy. The results show that sediment softness and area boundaries can be measured with this method, but obstacles for practical applications remain.
References
Allouch A, Koubâa A, Abbes T, Ammar A (2017) RoadSense: Smartphone application to estimate road conditions using accelerometer and gyroscope. IEEE Sensors Journal 17: 4231–4238. https://doi.org/10.1109/JSEN.2017.2702739
Austen I (1994) The surficial sediments of Königshafen - variations over the past 50 years. Helgoländer Meeresuntersuchungen 48: 163–171. https://doi.org/10.1007/BF02367033
Bennasar M, Price BA, Gooch D, Bandara AK, Nuseibeh B (2022) Significant features for human activity recognition using tri-axial accelerometers. Sensors 22: 7482. https://doi.org/10.3390/s22197482
Choi K (2024) One-tap sensor logger in your pocket. https://www.tszheichoi.com/sensorlogger (last access: 04 Jul 2025).
Denner-Möller E (1983) Untersuchungen zur digitalen multispektralen Klassifizierung von Fernerkundungsaufnahmen mit Beispielen aus dem Wattgebieten der deutschen Nordseeküste. PhD thesis. Hannover.
Dentamaro V, Gattulli V, Impedovo D, Manca F (2024) Human activity recognition with smartphone-integrated sensors: A survey. Expert Systems with Applications 246: 123143. https://doi.org/10.1016/j.eswa.2024.123143
Doerffer R, Murphy D (1989) Factor analysis and classification of remotely sensed data for monitoring tidal flats. Helgoländer Meeresuntersuchungen 43: 275–293. https://doi.org/10.1007/BF02365889
Figge K, Köster R, Thiel H, Wieland P (1980) Schlickuntersuchungen im Wattenmeer der Deutschen Bucht. Zwischenbericht über ein Forschungsprojekt der KFKI. Die Küste 35: 187–204.
Gade M, Kohlus J, Mertens C (2017) Archaeological surveys on the German North Sea coast using high-resolution synthetic aperture radar data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W2: 65–69. https://doi.org/10.5194/isprs-archives-XLII-3-W2-65-2017
Gast RE (1980) Die Sedimente der Meldorfer Bucht (Deutsche Bucht): Ihre Sedimentpetrographie und Besiedlung, Typisierung und Schwermetallgehalte. PhD thesis. Kiel.
Gupta A, Hu S, Zhong W, Sadek A, Su L, Qiao C (2020) Road grade estimation using crowd-sourced smartphone data. 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN): 313–324. Sydney. https://doi.org/10.1109/IPSN48710.2020.00-25
Köster R (1980) Geologisches Gutachten zu den geplanten Küstenschutzmaßnahmen im südlichen nordfriesischen Wattenmeer - Kurzfassung. Schriftenreihe der Landesregierung Schleswig-Holstein 12: 89–131. Kiel.
Köster R (1998) Wattsedimente. Kohlus J, Küpper H (eds): Umweltatlas Wattenmeer Bd. 1: 40–41. Stuttgart.
Kleeberg U (1990); Kartierung der Sedimentverteilung im Wattenmeer durch integrierte Auswertung von Satellitendaten und Daten der Wattenmeerdatenbank der GKSS. PhD thesis. Trier.
Kobayashi S, Hasegawa T (2021) Smartphone-based estimation of sidewalk surface type via deep learning. Sensors and Materials 33: 35–51 https://doi.org/10.18494/SAM.2021.2976
Lauer W-U, Naumann M, Zeiler M (2014) Erstellung der Karte zur Sedimentverteilung auf dem Meeresboden in der deutschen Nordsee nach der Klassifikation von Figge (1981). Dokumentation Nr. 1, Geopotenzial Deutsche Nordsee Modul B. https://download.bgr.de/bgr/geologie/GPDN-Sedimentverteilung/Dokumentation_Nr1_FIGGE_V2-1.pdf
Menz BH, Lord SR, Fitzpatrick RC (2003) Acceleration patterns of the head and pelvis when walking on level and irregular surfaces. Gait & Posture 18: 35–46. https://doi.org/10.1016/S0966-6362(02)00159-5
Müller G, Stelzer K, Smollich S, Gade M, Adolph W, Melchionna S, Kemme L, Geißler J, Millat G, Reimers H-C, Kohlus J, Eskildsen K (2016) Remotely sensing the German Wadden Sea – A new approach to address national and international environmental legislation. Environmental Monitoring and Assessment 188: 595. https://doi.org/10.1007/s10661-016-5591-x
Ng HR., Sossa I, Nam Y, Youn, JH (2023) Machine learning approach for automated detection of irregular walking surfaces for walkability assessment with wearable sensor. Sensors 23: 193. https://doi.org/10.3390/s23010193
Park DE, Youn JH, Song TS (2025) Automated sidewalk surface detection using wearable accelerometry and deep learning. Sensors 25: 4228. https://doi.org/10.3390/s25134228
Ranyal E, Sadhu A, Jain K (2022) Road condition monitoring using smart sensing and artificial intelligence: A Review. Sensors 22: 3044. https://doi.org/10.3390/s22083044
Shimizu H, Tanigawa K, Bandara A, Kawamoto S, Suzuki S, Nagai-Tanima M, Aoyama T (2025) Influence of surface type on outdoor gait parameters measured using an in-shoe motion sensor system. Medical Engineering & Physics 137: 104295. https://doi.org/10.1016/j.medengphy.2025.104295
Takhashi J, Kobana Y, Tobe Y, Lopez GF (2015) Classification of steps on road surface using acceleration signals. The Second International Workshop on Web Intelligence and Smart Sensing IWWISS https://doi.org/10.4108/eai.22-7-2015.2260293
Thies SB, Richardson JK, Ashton-Miller JA (2005) Effects of surface irregularity and lighting on step variability during gait: A study in healthy young and older women. Gait & Posture 22: 26–31. https://doi.org/10.1016/j.gaitpost.2004.06.004
UNESCO (2009) Report of Decisions. World Heritage Committee 09/33.COM/20, Seville, 20 July 2009. https://unesdoc.unesco.org/ark:/48223/pf0000217214
van Bernem K-H (1992) Thematische Kartierung und Sensitivitätsraster im deutschen Wattenmeer. Deutsche Hydrologische Zeitschrift 44: 293–309. https://doi.org/10.1007/BF02231714
van Bernem K-H (1998) Sensitivitätsraster im deutschen Wattenmeer. Kohlus J, Küpper H (eds): Umweltatlas Wattenmeer Bd. 1: 222–223. Stuttgart.
van Bernem K-H, Krasemann HL, Müller A, Patzig S, Riethmüller R, Grotjahn M, Knüpling J, Ramm G, Neugebohrn L, Suchrow S, Sach G (1994) Thematische Kartierung und Sensitivitätsraster im deutschen Wattenmeer Juni 1987-Juni 1993. Geesthacht.
Wang W, Gade M, Stelzer K, Kohlus J, Ziwei L (2021) A classification scheme for sediments and habitats on exposed intertidal flats with multi-frequency polarimetric SAR. Remote Sensing 13: 360. https://doi.org/10.3390/rs13030360
Wohlenberg E (1937) Die Wattenmeer-Lebensgemeinschaft im Königshafen von Sylt. Helgoländer Meeresuntersuchungen 1: 1–92. https://doi.org/10.1007/BF02285337
Zang K, Shen J, Huang H, Wan M, Shi J (2018); Assessing and mapping of road surface roughness based on GPS and accelerometer sensors on bicycle-mounted smartphones. Sensors 18: 914. https://doi.org/10.3390/s18030914
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Copyright (c) 2025 Jörn Kohlus, Friederike Nowak, Hannah Böhm, Ralf Kagelmann
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