{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:25:52Z","timestamp":1764588352344,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is \u201csliding\u201d forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.<\/jats:p>","DOI":"10.3390\/s22218472","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T04:00:51Z","timestamp":1667534451000},"page":"8472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Real-Time Step Length Estimation in Indoor and Outdoor Scenarios"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8106-9258","authenticated-orcid":false,"given":"Zanru","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2677-8721","authenticated-orcid":false,"given":"Le Chung","family":"Tran","sequence":"additional","affiliation":[{"name":"School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4322-4448","authenticated-orcid":false,"given":"Farzad","family":"Safaei","sequence":"additional","affiliation":[{"name":"School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9229-4557","authenticated-orcid":false,"given":"Anh Tuyen","family":"Le","sequence":"additional","affiliation":[{"name":"School of Electrical and Data Engineering, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5991-8858","authenticated-orcid":false,"given":"Attaphongse","family":"Taparugssanagorn","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"ref_1","first-page":"177","article-title":"Characteristics of the gait in old people who fall","volume":"2","author":"Guimaraes","year":"1980","journal-title":"Int. Rehabil. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.gaitpost.2010.06.013","article-title":"Independent influence of gait speed and step length on stability and fall risk","volume":"32","author":"Espy","year":"2010","journal-title":"Gait Posture"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1080\/00140130500478553","article-title":"Gait parameters as predictors of slip severity in younger and older adults","volume":"49","author":"Moyer","year":"2006","journal-title":"Ergonomics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.gaitpost.2010.10.004","article-title":"Predisability and gait patterns in older adults","volume":"33","author":"Verghese","year":"2011","journal-title":"Gait Posture"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Z.Q., and Yang, F. (2017). Obesity may not induce dynamic stability disadvantage during overground walking among young adults. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169766"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1111\/j.1532-5415.1999.tb05209.x","article-title":"Walking speed and stride length predicts 36 months dependency, mortality, and institutionalization in chinese aged 70 and older","volume":"47","author":"Woo","year":"1999","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1001\/jama.2010.1923","article-title":"Gait speed and survival in older adults","volume":"305","author":"Studenski","year":"2011","journal-title":"JAMA"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cabric, M. (2017). From Corporate Security to Commercial Force: A Business Leader\u2019s Guide to Security Economics, Butterworth-Heinemann.","DOI":"10.1016\/B978-0-12-805149-8.00002-9"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tran, L.C., Le, A.T., Huang, X., Phung, S.L., Ritz, C., and Bouzerdoum, A. (2021). Background on positioning and localization for social distancing. Enabling Technologies for Social Distancing: Fundamentals, Concepts and Solutions, The IET.","DOI":"10.1049\/PBTE104E_ch2"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1016\/S0025-6196(11)60684-8","article-title":"Eldercare technology for clinical practitioners","volume":"84","author":"Crane","year":"2009","journal-title":"Mayo Clin. Proc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2618","DOI":"10.1109\/TBME.2017.2653246","article-title":"Single-camera-based method for step length symmetry measurement in unconstrained elderly home monitoring","volume":"64","author":"Cai","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/S0966-6362(03)00068-7","article-title":"Reliability of the GAITRite\u00ae walkway system for the quantification of temporo-spatial parameters of gait in young and older people","volume":"20","author":"Menz","year":"2004","journal-title":"Gait Posture"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Srinivasan, P., Birchfield, D., Qian, G., and Kidan\u00e9, A. (2005, January 15\u201317). A pressure sensing floor for interactive media applications. Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, Valencia, Spain.","DOI":"10.1145\/1178477.1178526"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, E., Lin, X., Seet, B.-C., Joseph, F., and Neville, J. (2019, January 20\u201323). Low profile and low cost textile smart mat for step pressure sensing and position mapping. Proceedings of the IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Auckland, New Zealand.","DOI":"10.1109\/I2MTC.2019.8826892"},{"key":"ref_15","unstructured":"(2022, September 13). GAITRite: World Leader in Temporospatial Gait Analysis. Available online: https:\/\/www.gaitrite.com."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3362","DOI":"10.3390\/s140203362","article-title":"Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications","volume":"14","year":"2014","journal-title":"Sensors"},{"key":"ref_17","unstructured":"Wang, F., Stone, E., Dai, W., Skubic, M., and Keller, J. (2009, January 3\u20136). Gait analysis and validation using voxel data. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-642-02230-2_3","article-title":"Analyzing gait using a time-of-flight camera","volume":"Volume 5575","author":"Jensen","year":"2009","journal-title":"Image Analysis\u201416th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 2009"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tron, R., and Vidal, R. (2009, January 15\u201318). Distributed image-based 3-d localization of camera sensor networks. Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly with 2009 28th Chinese Control Conference, Shanghai, China.","DOI":"10.1109\/CDC.2009.5400405"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TNSRE.2010.2041792","article-title":"A monocular marker-free gait measurement system","volume":"18","author":"Courtney","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/978-3-642-24136-9_20","article-title":"Real-time upper-body human pose estimation using a depth camera","volume":"Volume 6930","author":"Jain","year":"2011","journal-title":"Computer Vision\/Computer Graphics Collaboration Techniques. MIRAGE 2011"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Aubeck, F., Isert, C., and Gusenbauer, D. (2011, January 21\u201323). Camera based step detection on mobile phones. Proceedings of the 2011 International Conference on Indoor Positioning and Indoor Navigation, Guimaraes, Portugal.","DOI":"10.1109\/IPIN.2011.6071910"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vishnoi, N., Duric, Z., and Gerber, N.L. (September, January 28). Markerless identification of key events in gait cycle using image flow. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6347077"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Das, S., and Meher, S. (2013, January 12\u201314). Automatic extraction of height and stride parameters for human recognition. Proceedings of the 2013 Students Conference on Engineering and Systems (SCES), Allahabad, India.","DOI":"10.1109\/SCES.2013.6547561"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, W., Anderson, B., Zhu, S., and Wang, Y. (2016, January 23\u201326). A computer vision-based system for stride length estimation using a mobile phone camera. Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility, Reno, NV, USA.","DOI":"10.1145\/2982142.2982156"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Singhal, S., Neustaedter, C., Schiphorst, T., Tang, A., Patra, A., and Pan, R. (2016, January 7\u201312). You are being watched: Bystanders\u2019 perspective on the use of camera devices in public spaces. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, San Jose, CA, USA.","DOI":"10.1145\/2851581.2892522"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"133725","DOI":"10.1109\/ACCESS.2021.3115808","article-title":"Indoor positioning using deep-learning-based pedestrian dead reckoning and optical camera communication","volume":"9","author":"Jeong","year":"2021","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/JBHI.2012.2233745","article-title":"Toward a passive low-cost in-home gait assessment system for older adults","volume":"17","author":"Wang","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2904","DOI":"10.1016\/j.patcog.2010.03.011","article-title":"Infrared gait recognition based on wavelet transform and support vector machine","volume":"43","author":"Xue","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/1743-0003-9-9","article-title":"Bilateral step length estimation using a single inertial measurement unit attached to the pelvis","volume":"9","author":"Cereatti","year":"2012","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1109\/10.605434","article-title":"Long-term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope","volume":"44","author":"Miyazaki","year":"1997","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Diaz, E.M., and Gonzalez, A.L.M. (2014, January 27\u201330). Step detector and step length estimator for an inertial pocket navigation system. Proceedings of the 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, Korea.","DOI":"10.1109\/IPIN.2014.7275473"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zihajehzadeh, S., and Park, E.J. (2016, January 16\u201320). Experimental evaluation of regression model-based walking speed estimation using lower body-mounted IMU. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590685"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, Z., Tran, L.C., and Safaei, F. (2022). Step length estimation using the RSSI method in walking and jogging scenarios. Sensors, 22.","DOI":"10.3390\/s22041640"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, Z., Tran, L.C., and Safaei, F. (2021). Step length measurements using the received signal strength ndicator. Sensors, 21.","DOI":"10.3390\/s21020382"},{"key":"ref_36","unstructured":"DIGI (2022, October 17). XBee\/XBee-PRO\u00ae s2c zigbee\u00ae RF Module User Guide. Available online: https:\/\/www.digi.com\/resources\/documentation\/digidocs\/pdfs\/90002002.pdf."},{"key":"ref_37","unstructured":"(2012). IEEE Standard for Local and Metropolitan Area Networks\u2014Part 15.6: Wireless Body Area Networks (Standard No. IEEE Std 802.15.6)."},{"key":"ref_38","unstructured":"Hauptman, N. (2022, October 17). The Average Walking Stride Length. Live Healthy. Available online: https:\/\/livehealthy.chron.com\/determine-stride-pedometer-height-weight-4518.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8472\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:10:13Z","timestamp":1760145013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,3]]},"references-count":38,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218472"],"URL":"https:\/\/doi.org\/10.3390\/s22218472","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,11,3]]}}}