Shapes of You? Investigating the Acceptance of Video-Based AAL Technologies Applying Different Visualization Modes
Abstract
:1. Introduction
1.1. Ambient-Assisted Living Applications and Video-Based Systems
1.2. Perception and Acceptance of Video-Based AAL Technologies
1.3. Privacy Perceptions as a Necessity of Privacy Preservation
1.4. Research Gaps, Aim, and Questions
- RQ1: How are video-based AAL technologies perceived and accepted in general?
- RQ2: Which specific visualization modes are selected for different exemplary situations of everyday life (in older age)?
- RQ3: Are there differences in the acceptance of the different visualization modes?
- RQ4: Are the visualization modes and their characteristics evaluated differently?
- RQ5: Are there user groups differing in the acceptance of video-based AAL technology? How are the groups and their evaluation patterns characterized?
2. Materials and Methods
2.1. Empirical Design
“The system described here aims at everyday support in older age and works with the help of cameras. The cameras are installed in the residents’ own homes and can record their daily lives. The cameras are about the size of a hand and are mounted at head height and in various rooms. They record only the image but no sound, so they cannot listen to conversations. The system and its cameras can be switched off and on by the user at any time. The cameras, or the system behind them, can offer users various health services. These include, for example, the detection of falls, support during rehabilitation (e.g., after an operation), remote monitoring by doctors or nurses, or the early detection of signs of dementia or frailty. The cameras continuously evaluate the video material. In this way, the system can independently detect an accident or emergency and then trigger an emergency call. With the user’s consent, it is also possible to record important events to learn why they happened - for example, what caused a fall. The more videos that are recorded, the more likely it is that the system will be able to detect, for example, whether a person is suddenly performing unusual movements or actions. If this indicates a risk, e.g., in the case of persons with incipient dementia, family members or the family doctor can be notified.”
“Using a lifelogging system in your own home comes with several advantages and disadvantages. For example, installing cameras may raise privacy concerns. Such concerns may arise because your family members, doctors, or caregivers may be watching you in situations where you do not want them to. To mitigate these privacy concerns, various video visualization modes have been developed to make people unrecognizable on the video. The following is a brief explanation of the filters and their characteristics. Please read this explanation carefully.
Imagine that you have decided to use a video-based AAL system for support in older age at home. The system now provides you with different visualization modes, each offering a different level of privacy and visibility. Before installing the cameras, you can select a mode that will make you unrecognizable on the video footage. Once installed, the cameras now record what’s happening in your home around the clock. The moment someone wants to access your videos, the visualization mode you selected will be activated. The access can be from one of your relatives, your doctor or nurse, for example, because they want to check your status or because the system has sent a warning - like in case of a fall. The visualization mode you choose now processes the recorded video so that your caregivers can only see the video in the way you want them to. In doing so, the visualization modes are designed to protect your privacy while allowing your family members, doctors, or caregivers to assess whether something has happened to you. The stronger a visualization mode is, the more detail is obscured from the image. While with some visualization modes you can still make out colors, structures and shapes, other modes only show the person’s posture or figure. The more details are rendered unrecognizable, the less information can be extracted from the video by relatives, doctors, or nurses.
The next step is to evaluate the visualization modes. For this purpose, short video sequences are shown below, which present the filters in different situations. As mentioned at the beginning, there are no wrong answers, you are asked for your personal feeling with regard to the videos and their visualization modes. We will start with a concrete application scenario of the lifelogging system and its cameras. Please watch the following video. Imagine that the camera is installed in your home and that you are the person being filmed here.”
2.2. Online Survey
2.3. Sample Description
2.4. Data Analysis
3. Results
3.1. Acceptance of Video-Based AAL Technology (RQ1)
3.2. Comparing Different Visualization Modes
3.2.1. Selecting Visualization Modes for Different Situations (RQ2)
3.2.2. Acceptance of Different Visualization Modes (RQ3)
3.2.3. Affective Evaluations of the Visualization Modes (RQ4)
3.3. Does User Diversity Make a Difference? (RQ5)
4. Discussion
4.1. Key Insights, Their Relevance, and Derived Implications
4.1.1. Influence of Application Contexts
4.1.2. Influence of Technical Characteristics
4.1.3. Influence of User Characteristics: User Diversity Makes a Difference
4.2. Conclusions, Limitations, and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Cluster 1 (n = 81) | Cluster 2 (n = 80) | Statistical Information | |
---|---|---|---|---|
Demographics | Age (M, SD) | 47.65 (19.18) | 46.08 (19.73) | F(1, 156) = 0.26 p = 0.61, n.s. |
Gender | 38.3% male 65.5% female | 37.5% male 62.5% female | F(1, 160) = 0.003 p = 0.95, n.s. | |
Health | Chronic Disease | 27.2% yes 72.8% no | 27.5% yes 72.5% no | F(1, 160) = 0.002 p = 0.96, n.s. |
Impairment/ restriction | 13.6% yes 86.4% no | 13.8% 86.2% | F(1, 160) = 0.001 p = 0.98, n.s. | |
Attitudinal Characteristics (M, SD) | Privacy Perception | 4.13 (0.90) | 4.57 (0.96) | F(1, 160) = 8.58 p < 0.01; |
Technology Commitment | 4.24 (0.96) | 3.92 (1.01) | F(1, 160) = 4.23 p < 0.05; |
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Offermann, J.; Wilkowska, W.; Maidhof, C.; Ziefle, M. Shapes of You? Investigating the Acceptance of Video-Based AAL Technologies Applying Different Visualization Modes. Sensors 2023, 23, 1143. https://doi.org/10.3390/s23031143
Offermann J, Wilkowska W, Maidhof C, Ziefle M. Shapes of You? Investigating the Acceptance of Video-Based AAL Technologies Applying Different Visualization Modes. Sensors. 2023; 23(3):1143. https://doi.org/10.3390/s23031143
Chicago/Turabian StyleOffermann, Julia, Wiktoria Wilkowska, Caterina Maidhof, and Martina Ziefle. 2023. "Shapes of You? Investigating the Acceptance of Video-Based AAL Technologies Applying Different Visualization Modes" Sensors 23, no. 3: 1143. https://doi.org/10.3390/s23031143
APA StyleOffermann, J., Wilkowska, W., Maidhof, C., & Ziefle, M. (2023). Shapes of You? Investigating the Acceptance of Video-Based AAL Technologies Applying Different Visualization Modes. Sensors, 23(3), 1143. https://doi.org/10.3390/s23031143