more modalities. These additions will not only enhance the realism of the synthetic data but also broaden the scope of activity recognition research within smart homes.

In our future work, we aim to investigate training the algorithm from scratch in a house without replicating the labeled activities of the final resident. Instead, we will solely utilize the activity scripts provided by our volunteers, enabling a more realistic and autonomous training process. By addressing these aspects in our future work, we aim to further validate and enhance the effectiveness of our approach for generating synthetic data and training HAR algorithms in smart homes.

By harnessing the concept of digital twins and generating realistic synthetic data, we effectively mitigate the challenges posed by limited datasets and the gap between training and real environment data, thereby enhancing the applicability of HAR algorithms in real-world smart home environments. Our study contributes to the development of a comprehensive tool and methodology for implementing digital twins in the context of smart homes, enabling the development of more effective ADL classification algorithms and ultimately improving the performance of smart home systems.

In conclusion, the results obtained from this work highlight the potential of digital twins in generating synthetic data and training HAR algorithms for smart homes. Our future research will focus on addressing the identified limitations and further validating the approach with larger and more diverse datasets from real smart homes. We firmly believe that our findings significantly contribute to the advancement of HAR technology, paving the way for more efficient and adaptive smart home systems.

**Author Contributions:** Conceptualization, D.B. and J.G.; methodology, D.B., S.M.N and C.L.; software, D.B. and J.G.; validation, D.B., J.G., S.M.N. and C.L.; formal analysis, D.B.; investigation, D.B.; data curation, D.B. and J.G.; writing—original draft preparation, D.B. and J.G.; writing—review and editing, D.B, J.G, S.M.N., C.L., X.P.; visualization, J.G.; supervision, S.M.N., C.L., X.P.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is partially supported by project VITAAL and is financed by Brest Metropole, the region of Brittany and the European Regional Development Fund (ERDF). This work is partially supported by the "plan France relance" of 21 December 2020 and a CIFRE agreement with the company Delta Dore in Bonemain 35270 France, managed by the National Association of Technical Research (ANRT) in France. We gratefully acknowledge the support of AID Project ACoCaTherm which supported the dataset creation.

**Institutional Review Board Statement:** Not applicable

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable

**Acknowledgments:** We would like to thank Jérôme Kerdreux for his support in this project.

**Conflicts of Interest:** The authors declare no conflict of interest.

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13. Barricelli, B.R.; Casiraghi, E.; Fogli, D. A survey on digital twin: Definitions, characteristics, applications, and design implications. *IEEE Access* **2019**, *7*, 167653–167671. [CrossRef]

14. Ngah Nasaruddin, A.; Ito, T.; Tee, B.T. Digital Twin Approach to Building Information Management. *Proc. Manuf. Syst. Div. Conf.* **2018**, *2018*, 304. [CrossRef]

15. Khajavi, S.; Hossein Motlagh, N.; Jaribion, A.; Werner, L.; Holmström, J. Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings. *IEEE Access* **2019**, *7*, 147406–147419. [CrossRef]

16. Puig, X.; Ra, K.; Boben, M.; Li, J.; Wang, T.; Fidler, S.; Torralba, A. Virtualhome: Simulating Household Activities via Programs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018; pp. 8494–8502.

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18. Gochoo, M.; Tan, T.H.; Liu, S.H.; Jean, F.R.; Alnajjar, F.S.; Huang, S.C. Unobtrusive activity recognition of elderly people living alone using anonymous binary sensors and DCNN. *IEEE J. Biomed. Health Inform.* **2018**, *23*, 693–702. [CrossRef]

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21. Cook, D.J.; Crandall, A.S.; Thomas, B.L.; Krishnan, N.C. CASAS: A Smart Home in a Box. *Computer* **2013**, *46*, 62–69. [CrossRef]

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23. Golestan, S.; Stroulia, E.; Nikolaidis, I. Smart Indoor Space Simulation Methodologies: A Review. *IEEE Sens. J.* **2022**, *22*, 8337–8359. [CrossRef]

24. Bruneau, J.; Consel, C.; OMalley, M.; Taha, W.; Hannourah, W.M. Virtual testing for smart buildings. In Proceedings of the 2012 Eighth International Conference on Intelligent Environments, Guanajuato, Mexico, 26–29 June 2012; pp. 282–289.

25. Kolve, E.; Mottaghi, R.; Han, W.; VanderBilt, E.; Weihs, L.; Herrasti, A.; Gordon, D.; Zhu, Y.; Gupta, A.; Farhadi, A. AI2-THOR: An Interactive 3D Environment for Visual AI. *arXiv* **2019**, arXiv:1712.05474.

26. Xia, F.; Zamir, A.R.; He, Z.; Sax, A.; Malik, J.; Savarese, S. Gibson Env: Real-World Perception for Embodied Agents. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018; pp. 9068–9079. [CrossRef]

27. Roitberg, A.; Schneider, D.; Djamal, A.; Seibold, C.; Reiß, S.; Stiefelhagen, R. Let’s Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games. *arXiv* **2021**, arXiv:2107.05617.

28. Savva, M.; Kadian, A.; Maksymets, O.; Zhao, Y.; Wijmans, E.; Jain, B.; Straub, J.; Liu, J.; Koltun, V.; Malik, J.; et al. Habitat: A Platform for Embodied AI Research. *arXiv* **2019**, arXiv:1904.01201.

29. Alshammari, N.; Alshammari, T.; Sedky, M.; Champion, J.; Bauer, C. OpenSHS: Open smart home simulator. *Sensors* **2017**, *17*, 1003. [CrossRef] [PubMed]

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31. Lee, J.W.; Cho, S.; Liu, S.; Cho, K.; Helal, S. Persim 3D: Context-Driven Simulation and Modeling of Human Activities in Smart Spaces. *IEEE Trans. Autom. Sci. Eng.* **2015**, *12*, 1243–1256. [CrossRef]
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**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
32. Synnott, J.; Chen, L.; Nugent, C.; Moore, G. IE Sim—A Flexible Tool for the Simulation of Data Generated within Intelligent Environments. In Proceedings of the Ambient Intelligence, Pisa, Italy, 13–15 November 2012; Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K., Eds.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2012; pp. 373–378.

33. Bouchard, K.; Ajroud, A.; Bouchard, B.; Bouzouane, A. SIMACT: A 3D Open Source Smart Home Simulator for Activity Recognition with Open Database and Visual Editor. *Int. J. Hybrid Inf. Technol.* **2012**, *5*, 13–32.

34. Park, B.; Min, H.; Bang, G.; Ko, I. The User Activity Reasoning Model in a Virtual Living Space Simulator. *Int. J. Softw. Eng. Its Appl.* **2015**, *9*, 53–62. [CrossRef]

35. Francillette, Y.; Boucher, E.; Bouzouane, A.; Gaboury, S. The Virtual Environment for Rapid Prototyping of the Intelligent Environment. *Sensors* **2017**, *17*, 2562. [CrossRef] [PubMed]

36. Buchmayr, M.; Kurschl, W.; Küng, J. A simulator for generating and visualizing sensor data for ambient intelligence environments. *Procedia Comput. Sci.* **2011**, *5*, 90–97. [CrossRef]

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38. VirtualHome. Available online: http://www.virtual-home.org/ (accessed on 21 January 2021).

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40. Roberts, M.; Ramapuram, J.; Ranjan, A.; Kumar, A.; Bautista, M.A.; Paczan, N.; Webb, R.; Susskind, J.M. Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding. *arXiv* **2021**, arXiv:2011.02523.

41. Srivastava, S.; Li, C.; Lingelbach, M.; Martín-Martín, R.; Xia, F.; Vainio, K.; Lian, Z.; Gokmen, C.; Buch, S.; Liu, C.K.; et al. BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments. *arXiv* **2021**, arXiv:2108.03332.

42. Shridhar, M.; Thomason, J.; Gordon, D.; Bisk, Y.; Han, W.; Mottaghi, R.; Zettlemoyer, L.; Fox, D. Alfred: A benchmark for interpreting grounded instructions for everyday tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 10740–10749.

43. Puig, X.; Shu, T.; Li, S.; Wang, Z.; Liao, Y.H.; Tenenbaum, J.B.; Fidler, S.; Torralba, A. Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration. In Proceedings of the International Conference on Learning Representations, Online, 3–7 May 2021.

44. Cao, Z.; Gao, H.; Mangalam, K.; Cai, Q.; Vo, M.; Malik, J. Long-term human motion prediction with scene context. In Proceedings of the ECCV, Glasgow, UK, 23–28 August 2020.

45. Varol, G.; Romero, J.; Martin, X.; Mahmood, N.; Black, M.J.; Laptev, I.; Schmid, C. Learning from Synthetic Humans. In Proceedings of the CVPR, Honolulu, HI, USA, 21–26 July 2017.

46. Synnott, J.; Nugent, C.; Jeffers, P. Simulation of smart home activity datasets. *Sensors* **2015**, *15*, 14162–14179. [CrossRef]

47. Kamara-Esteban, O.; Azkune, G.; Pijoan, A.; Borges, C.E.; Alonso-Vicario, A.; López-de Ipiña, D. MASSHA: An agent-based approach for human activity simulation in intelligent environments. *Pervasive Mob. Comput.* **2017**, *40*, 279–300. [CrossRef]

48. Helal, S.; Mann, W.; El-Zabadani, H.; King, J.; Kaddoura, Y.; Jansen, E. The gator tech smart house: A programmable pervasive space. *Computer* **2005**, *38*, 50–60. [CrossRef]

49. Puig, X. VirtualHome Source Code. Available online: https://github.com/xavierpuigf/virtualhome_unity (accessed on 25 January 2021).

50. Liao, Y.; Puig, X.; Boben, M.; Torralba, A.; Fidler, S. Synthesizing Environment-Aware Activities via Activity Sketches. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15–20 June 2019; pp. 6284–6292, ISSN: 2575-7075. [CrossRef]

51. VirtualHome Source Code and API. Available online: https://github.com/xavierpuigf/virtualhome (accessed on 25 January 2021).

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