Recognizing Hospital Care Activities with a Coat Pocket Worn Smartphone
Open accessAbstract
In this work, we show how a smart-phone worn unobtrusively in a nurse’s coat pocket can be used to document the patient care activities performed during a regular morning routine. The main contribution is to show how, taking into account certain domain specific boundary conditions, a single sensor
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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 possibility of performing a given activity at a given time is closely related to contextual information such as semantic location, objects involved, and previously executed activities.
“The idea is that the possibility of performing a given activity at a given time is closely related to contextual information such as (semantic) location, objects involved as well as previously executed activities (e.g., [8]).”
From Past, Present, and Future of Sensor-based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task (2025) · cites this paper as [8]
In “Building a Model” (page unavailable), the paper says its workflow model restricts activities by location and possible sequence, including what can follow a prior activity. It does not establish that the objects involved determine whether an activity is possible; the Conclusion and Discussion mentions relations among objects only as part of a proposed future semantic model. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147 · see citing works
Real-time American sign language recognition using desk and wearable computer based video
Starner, T., Weaver, J., & Pentland, A. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.735811 · Cited by 1,335 · see citing works
There is more to context than location
Schmidt, A., Beigl, M., & Gellersen, H. W. (1999) · Computers & Graphics · doi:10.1016/s0097-8493(99)00120-x · Cited by 1,089 · see citing works
A Hybrid Discriminative/Generative Approach for Modeling Human Activities
Lester, J., Choudhury, T., Kern, N., Borriello, G., & Hannaford, B. (2005) · TUbilio (Technical University of Darmstadt) · Cited by 402 · see citing works
Towards pervasive computing in health care – A literature review
Orwat, C., Graefe, A., & Faulwasser, T. (2008) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-8-26 · Cited by 232 · see citing works
Location-Based Activity Recognition
Fox, D. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74565-5_6 · Cited by 216 · see citing works
A Pervasive Computing System for the Operating Room of the Future
Agarwal, S. K., Joshi, A., Finin, T., et al. (2007) · Mobile Networks and Applications · doi:10.1007/s11036-007-0010-8 · Cited by 115 · see citing works
Seeger, C., Buchmann, A., & Laerhoven, K. V. (2011) · International Conference on Body Area Networks · doi:10.4108/icst.bodynets.2011.247015 · Cited by 93 · see citing works
Activity Recognition for Everyday Life on Mobile Phones
Bieber, G., Voskamp, J., & Urban, B. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02710-9_32 · Cited by 88 · see citing works
Real world activity recognition with multiple goals
Hu, D. H., Pan, S. J., Zheng, V. W., et al. (2008) · ACM International Conference on Ubiquitous Computing (UbiComp) · doi:10.1145/1409635.1409640 · Cited by 71 · see citing works
Blanke, U., & Schiele, B. (2010) · International Symposium on Wearable Computers · doi:10.1109/iswc.2010.5665869 · Cited by 69 · see citing works
Implementation and Evaluation of a Low-Power Sound-Based User Activity Recognition System
Stäger, M., Lukowicz, P., & Tröster, G. (2004) · International Symposium on Wearable Computers · doi:10.1109/iswc.2004.25 · Cited by 59 · see citing works
Self-citationAn Analysis of Sensor-Oriented vs. Model-Based Activity Recognition
Zinnen, A., Blanke, U., & Schiele, B. (2009) · International Symposium on Wearable Computers · doi:10.1109/iswc.2009.32 · Cited by 48 · see citing works
What Can an Arm Holster Worn Smart Phone Do for Activity Recognition?
Muehlbauer, M., Bahle, G., & Lukowicz, P. (2011) · International Symposium on Wearable Computers · doi:10.1109/iswc.2011.23 · Cited by 45 · see citing works
Self-citationUsing a complex multi-modal on-body sensor system for activity spotting
Ogris, G., Stiefmeier, T., Lukowicz, P., et al. (2008) · Proceedings · doi:10.1109/iswc.2008.4911585 · Cited by 42 · see citing works
Self-citationMulti Activity Recognition Based on Bodymodel-Derived Primitives
Zinnen, A., Wojek, C., & Schiele, B. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01721-6_1 · Cited by 41 · see citing works
Wearable computing and artificial intelligence for healthcare applications
Lukowicz, P. (2008) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2007.12.002 · Cited by 30 · see citing works
Self-citationLocati[o]n-based activity recognition
Liao, L. (2006) · Cited by 21 · see citing works
Does Context Matter ? - A Quantitative Evaluation in a Real World Maintenance Scenario
Kunze, K. S., Wagner, F., Kartal, E., Kluge, E. M., & Lukowicz, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01516-8_25 · Cited by 19 · see citing works
Self-citationAltakouri, B., Kortuem, G., Grünerbl, A., et al. (2010) · International Symposium on Wearable Computers · doi:10.1109/iswc.2010.5665866 · Cited by 17 · see citing works
Cheng, M., Kanai‐Pak, M., Kuwahara, N., et al. (2012) · International Journal of Autonomous and Adaptive Communications Systems · doi:10.1504/ijaacs.2012.044783 · Cited by 5 · see citing works
Wireless sensor network system for supporting nursing context-awareness
Naya, F., Ohmura, R., Miyamae, M., Noma, H., Kogure, K., & Imai, M. (2011) · International Journal of Autonomous and Adaptive Communications Systems · doi:10.1504/ijaacs.2011.043477 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- M. Cheng, M. KanaiPak, N. Kuwahara, H. Ozaku, K. Kogure, and J. Ota, "Dynamic schedulingbased inpatient nursing support: applicabil- ity evaluation by laboratory experiments," Journal International Journal of Autonomous and Adaptive Communications Systems, vol. 5, no. 1, pp. 39 -57, 2012.
Cited by
Sorted by citations
Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Plötz, T. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3729467 · Cited by 33 · see citing works
“Thus for example in [7] it has been shown that complex activities in a nursing scenario can be recognized using a mobile phone in a pocket of a loose nurse’s coat (which is a very poor source of information) by integrating high-level semantic knowledge about the activities in the recognition…”
Self-citationHaresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Ploetz, T. (2024) · arXiv · Cited by 31 · see citing works
“Thus for example in [7] it has been shown that complex activities in a nursing scenario can be recognized using a mobile phone in a pocket of a loose nurse’s coat (which is a very poor source of information) by integrating high-level semantic knowledge about the activities in the recognition…”
Self-citation
- Authors
- Bahle, G., Gruenerbl, A., Bignotti, E., Zeni, M., Giunchiglia, F., & Lukowicz, P.
- Affiliations
- German Research Centre for Artificial Intelligence (DE)
- University of Trento (IT)
- Corresponding author
- Bahle, Gernot
- Venue
- Proceedings of the 6th International Conference on Mobile Computing, Applications and Services
- Publisher
- ICST
- Published
- 2014
- OpenAlex
- W2542719558
- Cited by
- 15
- References
- 22
- Citations by year
- 2025: 1 · 2024: 2 · 2021: 2 · 2020: 3 · 2019: 3 · 2018: 1 · 2017: 1 · 2016: 1 · 2015: 1
- FWCI
- 0.54
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Mobile Computing, Applications, and Services (publishedVersion)
- Unitn Eprints Research (Università Degli Studi di Trento) (acceptedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
- Iris (University of Trento) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- activity recognition, patient care documentation, morning routine
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
