Development of Audio Sensing Technology for Ambient Assisted Living: Applications and Challenges
Open accessAbstract
One of the greatest challenges in Ambient Assisted Living is to design health smart homes that anticipate the needs of its inhabitant while maintaining their safety and comfort. It is thus essential to ease the interaction with the smart home through systems that naturally react to voice command using microphones rather than tactile interfaces. However, efficient audio analysis in such noisy environment is a challenging task. In this paper, a real-time audio analysis system, the AuditHIS system, devoted to audio analysis in smart home environment is presented. AuditHIS has been tested thought three experiments carried out in a smart home that are detailed. The results show the difficulty of the task and serve as basis to discuss the stakes and the challenges of this promising technology in the domain of AAL.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The audio channel shows promise towards providing security, comfort and health related assistance in smart homes, but it remains relatively unexplored compared to classical and mobile physical interfaces.
“These studies showed that the audio channel shows promise towards providing security, comfort and health related assistance in smart homes (Vacher et al., 2011), but it remains relatively unexplored compared to classical and mobile physical interfaces such as switches, remote control and mobile phones.”
From Making Emergency Calls More Accessible to Older Adults Through a Hands-free Speech Interface in the House (2019) · cites this paper as Vacher et al., 2011
Cites
Efficient duration and hierarchical modeling for human activity recognition
Duong, T., Phung, D., Bui, H., & Venkatesh, S. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2008.12.005 · Cited by 108 · cited 1× · see citing works
“Therefore, activity recognition is an active research area (Duong et al., 2009)(Vacher et al., 2010a) but, despite this, it has still not reached a satisfactory performance nor led to a standard methodology given the high number of flat configurations.”
BackgroundAcoustic models of the elderly for large-vocabulary continuous speech recognition
Baba, A., Yoshizawa, S., Yamada, M., Lee, A., & Shikano, K. (2004) · Electronics and Communications in Japan (Part II Electronics) · doi:10.1002/ecjb.20101 · Cited by 45 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Portet, F., Vacher, M., Golanski, C., Roux, C., & Meillon, B. (2013) · Personal and Ubiquitous Computing · doi:10.1007/s00779-011-0470-5 · Cited by 421 · see citing works
Self-citationKim, S. (2021) · JMIR mHealth and uHealth · doi:10.2196/20427 · Cited by 153 · see citing works
Vacher, M., Caffiau, S., Portet, F., Meillon, B., Roux, C., Elias, E., Lecouteux, B., & Chahuara, P. (2015) · ACM Transactions on Accessible Computing · doi:10.1145/2738047 · Cited by 73 · see citing works
Self-citationHobbit: Providing Fall Detection and Prevention for the Elderly in the Real World
Bajones, M., Fischinger, D., Weiss, A., Wolf, D., Vincze, M., de la Puente, P., Körtner, T., Weninger, M., Papoutsakis, K., Michel, D., et al. (2018) · Journal of Robotics · doi:10.1155/2018/1754657 · Cited by 64 · see citing works
Vacher, M., Aman, F., Rossato, S., Portet, F., & Lecouteux, B. (2019) · ACM Transactions on Accessible Computing · doi:10.1145/3310132 · Cited by 10 · see citing works
Self-citation
- Authors
- Vacher, M., Portet, F., Fleury, A., & Noury, N.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Université Lille Nord de France (FR)
- Université de Lyon (FR)
- Venue
- International Journal of E-Health and Medical Communications
- Publisher
- IGI Global Scientific Publishing
- Published
- 2011-01-01
- Volume
- 2
- Issue
- 1
- Pages
- 35-54
- ISSN
- 1947-315X (ISSN-L)
- 1947-3168
- OpenAlex
- W2089800227
- Cited by
- 74
- References
- 2
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 3 · 2021: 4 · 2020: 3 · 2019: 3 · 2018: 5 · 2017: 8 · 2016: 3 · 2015: 10 · 2014: 8 · 2013: 12 · 2012: 7
- FWCI
- 14.35
- Type
- journal article
- Language
- ng
- Open access
- green
- hal.science
- Licence
- other-oa
- Locations
- International Journal of E-Health and Medical Communications (publishedVersion)
- HAL (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Keywords
- ambient assisted living, home automation, audio sensing, voice command recognition, real-time audio analysis, noisy environments, health monitoring, elderly care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
