{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T13:43:50Z","timestamp":1772113430585,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T00:00:00Z","timestamp":1626134400000},"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>Human activity recognition aims to classify the user activity in various applications like healthcare, gesture recognition and indoor navigation. In the latter, smartphone location recognition is gaining more attention as it enhances indoor positioning accuracy. Commonly the smartphone\u2019s inertial sensor readings are used as input to a machine learning algorithm which performs the classification. There are several approaches to tackle such a task: feature based approaches, one dimensional deep learning algorithms, and two dimensional deep learning architectures. When using deep learning approaches, feature engineering is redundant. In addition, while utilizing two-dimensional deep learning approaches enables to utilize methods from the well-established computer vision domain. In this paper, a framework for smartphone location and human activity recognition, based on the smartphone\u2019s inertial sensors, is proposed. The contributions of this work are a novel time series encoding approach, from inertial signals to inertial images, and transfer learning from computer vision domain to the inertial sensors classification problem. Four different datasets are employed to show the benefits of using the proposed approach. In addition, as the proposed framework performs classification on inertial sensors readings, it can be applied for other classification tasks using inertial data. It can also be adopted to handle other types of sensory data collected for a classification task.<\/jats:p>","DOI":"10.3390\/s21144787","type":"journal-article","created":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T22:25:31Z","timestamp":1626215131000},"page":"4787","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["INIM: Inertial Images Construction with Applications to Activity Recognition"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0939-3379","authenticated-orcid":false,"given":"Nati","family":"Daniel","sequence":"first","affiliation":[{"name":"Technion-Israel Institute of Technology, 1st Efron st., Haifa 3525433, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7846-0654","authenticated-orcid":false,"given":"Itzik","family":"Klein","sequence":"additional","affiliation":[{"name":"Department of Marine Technologies, University of Haifa, 199 Aba Khoushy Ave., Haifa 3498838, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TSMCA.2011.2116004","article-title":"A framework for hand gesture recognition based on accelerometer and EMG sensors","volume":"41","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1109\/JSEN.2011.2166953","article-title":"MEMS accelerometer based nonspecific-user hand gesture recognition","volume":"12","author":"Xu","year":"2011","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Taylor, W., Shah, S.A., Dashtipour, K., Zahid, A., Abbasi, Q.H., and Imran, M.A. (2020). An intelligent non-invasive real-time human activity recognition system for next-generation healthcare. Sensors, 20.","DOI":"10.3390\/s20092653"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vepakomma, P., De, D., Das, S.K., and Bhansali, S. (2015, January 9\u201312). A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA.","DOI":"10.1109\/BSN.2015.7299406"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"015203","DOI":"10.1088\/0957-0233\/20\/1\/015203","article-title":"Activity classification and dead reckoning for pedestrian navigation with wearable sensors","volume":"20","author":"Sun","year":"2008","journal-title":"Meas. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Guo, S., Xiong, H., Zheng, X., and Zhou, Y. (2017, January 23\u201328). Indoor pedestrian trajectory tracking based on activity recognition. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128396"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep learning for sensor-based activity recognition: A survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_8","unstructured":"Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., and Havinga, P. (2010, January 22\u201323). Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. Proceedings of the 23th International Conference on Architecture of Computing Systems, Hannover, Germany."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/TSMCC.2012.2198883","article-title":"Sensor-based activity recognition","volume":"42","author":"Chen","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107561","DOI":"10.1016\/j.patcog.2020.107561","article-title":"Sensor-based and vision-based human activity recognition: A comprehensive survey","volume":"108","author":"Dang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.3390\/s150102059","article-title":"A survey of online activity recognition using mobile phones","volume":"15","author":"Shoaib","year":"2015","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1002\/navi.120","article-title":"Online motion mode recognition for portable navigation using low-cost sensors","volume":"62","author":"Elhoushi","year":"2015","journal-title":"Navig. J. Inst. Navig."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7577","DOI":"10.1109\/JSEN.2018.2861395","article-title":"Pedestrian dead reckoning with smartphone mode recognition","volume":"18","author":"Klein","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.1109\/JSEN.2017.2737825","article-title":"Learning transportation modes from smartphone sensors based on deep neural network","volume":"17","author":"Fang","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6429","DOI":"10.1109\/JIOT.2020.2985082","article-title":"Deep-learning-enhanced human activity recognition for Internet of healthcare things","volume":"7","author":"Zhou","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Asraf, O., Shama, F., and Klein, I. (2021). PDRNet: A Deep-Learning Pedestrian Dead Reckoning Framework. IEEE Sens. J.","DOI":"10.1109\/JSEN.2021.3066840"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, C., Lu, X., Markham, A., and Trigoni, N. (2018, January 2\u20137). Ionet: Learning to cure the curse of drift in inertial odometry. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12102"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Q., Ye, L., Luo, H., Men, A., Zhao, F., and Huang, Y. (2019). Pedestrian stride-length estimation based on LSTM and denoising autoencoders. Sensors, 19.","DOI":"10.3390\/s19040840"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"85706","DOI":"10.1109\/ACCESS.2020.2993534","article-title":"StepNet\u2014Deep learning approaches for step length estimation","volume":"8","author":"Klein","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9341","DOI":"10.1109\/JSEN.2021.3053843","article-title":"Attitude Adaptive Estimation with Smartphone Classification for Pedestrian Navigation","volume":"21","author":"Vertzberger","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zheng, X., Wang, M., and Ordieres-Mer\u00e9, J. (2018). Comparison of data preprocessing approaches for applying deep learning to human activity recognition in the context of industry 4.0. Sensors, 18.","DOI":"10.3390\/s18072146"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kirichenko, L., Radivilova, T., Bulakh, V., Zinchenko, P., and Alghawli, A.S. (2020, January 21\u201325). Two Approaches to Machine Learning Classification of Time Series Based on Recurrence Plots. Proceedings of the 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP47368.2020.9204021"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hou, Y., Zhou, S., and Ouyang, K. (2020). Encoding time series as multi-scale signed recurrence plots for classification using fully convolutional networks. Sensors, 20.","DOI":"10.3390\/s20143818"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rajabi, R., and Estebsari, A. (2019, January 23\u201327). Deep Learning Based Forecasting of Individual Residential Loads Using Recurrence Plots. Proceedings of the 2019 IEEE Milan PowerTech, Milan, Italy.","DOI":"10.1109\/PTC.2019.8810899"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wagner, D., Kalischewski, K., Velten, J., and Kummert, A. (2017, January 13\u201315). Activity recognition using inertial sensors and a 2-D convolutional neural network. Proceedings of the 2017 10th International Workshop on Multidimensional (nD) Systems (nDS), Zielona G\u00f3ra, Poland.","DOI":"10.1109\/NDS.2017.8070615"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ito, C., Cao, X., Shuzo, M., and Maeda, E. (2018, January 8\u201312). Application of CNN for human activity recognition with FFT spectrogram of acceleration and gyro sensors. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore.","DOI":"10.1145\/3267305.3267517"},{"key":"ref_27","unstructured":"Wang, Z., and Oates, T. (2015). Imaging time-series to improve classification and imputation. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.inffus.2019.06.014","article-title":"Imaging and fusing time series for wearable sensor-based human activity recognition","volume":"53","author":"Qin","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hur, T., Bang, J., Lee, J., Kim, J.I., and Lee, S. (2018). Iss2Image: A novel signal-encoding technique for CNN-based human activity recognition. Sensors, 18.","DOI":"10.3390\/s18113910"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","article-title":"Wider or Deeper: Revisiting the ResNet Model for Visual Recognition","volume":"90","author":"Wu","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"10146","DOI":"10.3390\/s140610146","article-title":"Fusion of Smartphone Motion Sensors for Physical Activity Recognition","volume":"14","author":"Shoaib","year":"2014","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sztyler, T., and Stuckenschmidt, H. (2016, January 14\u201319). On-body localization of wearable devices: An investigation of position-aware activity recognition. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), Sydney, Australia.","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 18\u201322). Introducing a New Benchmarked Dataset for Activity Monitoring. Proceedings of the 2012 16th International Symposium on Wearable Computers, Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Klein, I. (2020). Smartphone location recognition: A deep learning-based approach. Sensors, 20.","DOI":"10.3390\/s20010214"},{"key":"ref_37","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4787\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:29:52Z","timestamp":1760164192000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4787"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,13]]},"references-count":37,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21144787"],"URL":"https:\/\/doi.org\/10.3390\/s21144787","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,13]]}}}