Abstract
Anomalies are cases of an active class that vary in their performance from the standard or planned sequence of events. Early identification of body sensor networks is an essential and difficult role and has many possible benefits. Both these tasks are very important for the monitoring of suspicious behavior to capture human activities and evaluate these results. Data collection is important if the true evidence of human behavior is to be discovered. Since it uses the technique of state table transformation to find suspicious behaviors, identify them and track them from a distant location for elderly or children. This article offers an automatic activity surveillance system that identifies common activities that usually occur in a human routine. SVM is used here E-MultiClass (Support Vector Machine). It is used to rate two data groups with a binary score. This paper describes our task of data acquisition, recording them in the field of imagery processing and validating our algorithms on data obtained from remote sensors. It is also used to detect improvements in human routine as well as everyday life across these capabilities. It describes our activities in the field of image processing.
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References
Beretta, I.: Te social effects of eco-innovations in Italian smart cities. Cities 72, 115–121 (2018)
Kim, B.: A distributed coexistence mitigation scheme for IoT based smart medical systems. J. Inform. Process. Syst. 13(6), 1602–1612 (2017)
Sharma, P.K., Moon, S.Y., Park, J.H.: Block-VN: a distributed blockchain based vehicular network architecture in smart city. J. Inform. Process. Syst. 13(1), 184–195 (2017)
Jo, H., Yoon, Y.I.: Intelligent smart home energy efficiency model using artificial TensorFlow engine. HCIS 8(1), 1–18 (2018)
Sharma, P.K., Ryu, J.H., Park, K.Y.J.H., Park, J.H.: Li-Fi based on security cloud framework for future IT environment. HCIS 8(1), 23–36 (2018)
Ibrahim, Z., Rahim, F.A., Mokhtar, S.: Digital forensics issues in advanced metering infrastructure. J. Fundamental Appl. Sci. 10(6S), 2714–2726 (2018)
Wu, X., Chu, Z., Yang, P., Xiang, C., Zheng, X., Huang, W.: TW-See: human activity recognition through the wall with commodity Wi-Fi devices. IEEE Trans. Veh. Technol. 68(1), 306–319 (2019)
Passerini, F., Tonello, A.M.: Smart grid monitoring using power line modems: anomaly detection and localization. IEEE Trans. Smart Grid 7(1), 27302–27312 (2019)
Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Pattern Recogn. Lett. 119(1), 3–11 (2019)
Siano, P.: Demand response and smart grids—a survey. Renew. Sustain. Energy Rev. 30(5), 461–478 (2014)
Kelly, J., Knottenbelt, W.: Te UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from fve UK homes. Sci. Data 2(1), 150007–150021 (2015)
Malasinghe, L.P., Ramzan, N., Dahal, K.: Remote patient monitoring: a comprehensive study. J. Ambient. Intell. Humaniz. Comput. 10(1), 57–76 (2019)
Nweke, H.F., Teh, Y.W., Mujtaba, G., Al-Garadi, M.A.: Data fusion and multiple classifer systems for human activity detection and health monitoring: review and open research directions. Inform. Fusion 46(1), 147–170 (2019)
Hossain, M.S.: Patient state recognition system for healthcare using speech and facial expressions. J. Med. Syst. 40(12), 272–280 (2016)
De, P., Chatterjee, A., Rakshit, A.: Recognition of human behavior for assisted living using dictionary learning approach. IEEE Sens. J. 18(6), 2434–2441 (2018)
Ghayvat, H., Mukhopadhyay, S., Shenjie, B., Chouhan, A., Chen, W.: Smart home based ambient assisted living: recognition of anomaly in the activity of daily living for an elderly living alone. In: Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2018), pp. 1–5. MTC, May 2018
Ye, J., Stevenson, G., Dobson, S.: Detecting abnormal events on binary sensors in smart home environments. Pervasive Mob. Comput. 33, 32–49 (2016)
Skocir, P., Krivic, P., Tomeljak, M., Kusek, M., Jezic, G.: Activity detection in smart home environment. Procedia Comput. Sci. 96(1), 672–681 (2016)
Lu, L., Qing-ling, C., Yi-Ju, Z.: Activity recognition in smart homes. Multimedia Tools Appl. 76(22), 24203–24220 (2017)
Yao, L., Sheng, Q.Z., Benatallah, B., et al.: WITS: an IoTendowed computational framework for activity recognition in personalized smart homes. Computing 100(4), 369–385 (2018)
Hassan, M.M., Uddin, M.Z., Mohamed, A., Almogren, A.: A robust human activity recognition system using smartphone sensors and deep learning. Futur. Gener. Comput. Syst. 81(1), 307–313 (2018)
Sendra, S., Parra, L., Lloret, J., Tomas, J.: Smart system for ´ children’s chronic illness monitoring. Inform. Fusion 40(1), 76–86 (2018)
Pham, M., Mengistu, Y., Do, H., Sheng, W.: Delivering home healthcare through a cloud-based smart home environment (CoSHE). Futur. Gener. Comput. Syst. 81(1), 129–140 (2018)
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Kshirsagar, A.P., Shakkeera, L. (2022). Recognizing Abnormal Activity Using MultiClass SVM Classification Approach in Tele-health Care. In: Senjyu, T., Mahalle, P., Perumal, T., Joshi, A. (eds) IOT with Smart Systems. Smart Innovation, Systems and Technologies, vol 251. Springer, Singapore. https://doi.org/10.1007/978-981-16-3945-6_73
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DOI: https://doi.org/10.1007/978-981-16-3945-6_73
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