@article{7159,
  abstract     = {Background: Smart home technology powered by AI can detect anomalies and make emergency calls, enabling residents to live safely and independently. However, the adoption of such technologies for medical emergency detection remains limited.

Objective: This study aimed to explore consumer preferences for AI-based smart home technology for medical emergency detection and identify predictors such as sociodemographic variables, AI literacy, and technology affinity.

Method: A sample of 300 participants (172/300, 57.33% female; 128/300, 42.67% male, aged 18‐69 years) completed a choice-based conjoint analysis (CBCA). Participants evaluated 15 choice sets describing smart home variants based on cost, location, emergency detection rate, type of sensor, and data processing.

Results: Cost was the most important attribute (relative importance [RI]=41%), followed by emergency detection rate (RI=19%), data processing (RI=14%), and location (RI=14%). The type of sensor was the least important attribute (RI=9%). The preferred configuration combined an annual subscription of €70 (US $82), a 95% detection rate, wearable sensors, personalized AI, and installation in both intimate and shared rooms. Notably, 68.3% (205/300) of participants showed a positive none utility, indicating that even the optimal configuration did not overcome general reluctance to adopt such systems. While most expected correlations between sociodemographic variables and attribute importances were not observed, a significant correlation between self-reported health status and emergency detection rate was found (r=.16, P=.007). Interestingly, 61% (167/270) of participants preferred AI over human involvement in data processing, but logistic regression revealed that a significant correlation between self-reported health status and emergency detection rate was found (OR=0.58, P=.04).

Conclusions: These findings highlight the need to align smart home development with user preferences, emphasizing cost-effectiveness. Additionally, AI literacy plays an important role in technology adoption in the context of AI-based smart home technology. Further research is needed to understand and address the reluctance to adopt AI for medical emergency detection.},
  author       = {Jagemann, Inga and Baudisch, Justin and Jungeblut, Thorsten and Maier, Günter W and Hirschfeld, Gerrit},
  issn         = {2817-1705},
  journal      = {JMIR AI},
  pages        = {e94589--e94589},
  publisher    = {JMIR Publications Inc.},
  title        = {{Consumer Preferences for AI-Based Smart Home Medical Emergency Detection Among German Adults: Choice-Based Conjoint Analysis}},
  doi          = {10.2196/94589},
  volume       = {5},
  year         = {2026},
}

@article{6240,
  author       = {Jagemann, Inga and Baudisch, Justin and Jungeblut, Thorsten and Maier, Günter W. and Hirschfeld, Gerrit},
  journal      = {PsychArchives},
  publisher    = {PsychArchives},
  title        = {{The More You Know, the Less You Want to Rely on It – Consumer preferences for AI-based health monitoring in smart home systems}},
  doi          = {10.23668/PSYCHARCHIVES.21275},
  year         = {2025},
}

@article{6139,
  author       = {Attaullah, Hasina and Sanaullah, Sanaullah and Peters, Annika and Ahmed, Qazi Arbab and Baudisch, Justin and Jungeblut, Thorsten},
  issn         = {1662-453X},
  journal      = {Frontiers in Neuroscience},
  publisher    = {Frontiers Media SA},
  title        = {{DNA: detecting early signs of neurodegenerative diseases through activity and sleep analysis}},
  doi          = {10.3389/fnins.2025.1617758},
  volume       = {19},
  year         = {2025},
}

@inproceedings{4639,
  author       = {Herzig, Tim Christian and Marschner, Christian and Held, Stephan  and Jungeblut, Thorsten and Baudisch, Justin and Ostrau, Christoph and Klemm, Michael and Uphaus, Andreas and Amelung, Rena and Nauerth, Annette},
  booktitle    = {Kongress KI@HSBI2023 Solutions im Fokus - Posterbeiträge },
  issn         = {2943-3509 },
  location     = {Bielefeld},
  pages        = {9--10},
  publisher    = {Hochschule Bielefeld},
  title        = {{KI-gestütztes Monitoring zur Unterstützung in der häuslichen Pflege (KIMUP) }},
  doi          = {10.60802/sidas.2024.1 },
  volume       = {1},
  year         = {2024},
}

@article{4482,
  author       = {Baudisch, Justin and Richter, Birte and Jungeblut, Thorsten},
  issn         = {1610-1987},
  journal      = {KI - Künstliche Intelligenz},
  keywords     = {Ambient assisted living, anomaly detection, Internet of Things, Explainable AI},
  number       = {3-4},
  pages        = {259--266},
  publisher    = {Springer Science and Business Media LLC},
  title        = {{A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment}},
  doi          = {10.1007/s13218-022-00775-5},
  volume       = {36},
  year         = {2022},
}