The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Smart Cup to Monitor Stroke Patients Activities During Everyday Life
Abstract
This paper presents a new platform to monitor stroke patients activities during their everyday life at home. This platform is intended to be a part of a smart objects ecosystem for home monitoring using common objects embedding sensors. The monitoring is performed with a self-contained smart cup that can be used to drink at different times of the day. The smart cup embeds various sensors in order to detect its movements and the liquid level. Activity analysis is performed on the collected data in order to provide information to the therapists on the patient's sedentariness and independence on the daily life tasks (sitting, walking, drinking and going up and down the stairs). This paper presents the design concept of the smart cup along with the implementation and mainly focuses on the activity analysis process. We used a linear classifier: the Support Vector Machine (SVM) classifier. Indeed, the classification of stroke patient's activities is a binary classification. Moreover, as we decided to use DCT features, SVM is the classifier that gives better classification performances. The results show a recognition precision above 92% on all activities with the smart cup. A comparative study has been carried out in order to assess the performances of the linear SVM classifier and a non-linear Multi-Layered Perceptron (MLP) classifier. The result of this study shows that the linear SVM classifier offers better performances on classifying everyday life activities with a smart cup.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“performing kitchen tasks like making a drink, chopping food [42] and other routine activities like making the bed, reading and lacing shoes [48], folding, sweeping and brushing teeth [46,48,49].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [42]
In the citing paper
“Other works only used AB [41,42,50]”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [42]
Citations
Cites
Capela, N. A., Lemaire, E. D., & Baddour, N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124414 · Cited by 197
Research works printed but not linked: 25
- 10.1109/IEMBS.2010.5627577 · DOI
- 10.1161/STROKEAHA.110.611095 · DOI
- 10.1145/1514095.1514187 · DOI
- 10.1145/2647868.2655034 · DOI
- 10.1109/ICSMC.2009.5346042 · DOI
- ravi, 2005, Activity recognition from accelerometer data, AAAI, 5, 1541
- 10.1007/978-3-642-35395-6_30 · DOI
- 10.4108/icst.pervasivehealth.2015.259044 · DOI
- 10.1145/1363686.1364104 · DOI
- gao, 2012, A comparison of classifiers for activity recognition using multiple accelerometer-based sensors, Cybernetic Intelligent Systems (CIS) 2012 IEEE 11th International Conference on, 149
- soucies, 2014, Smart moving nightstand-for medical assistance of elderly people an open project, HEALTHINF, 517
- gialanella, 2013, Predicting outcome after stroke the role of basic activities of daily living predicting outcome after stroke, 49, 629
- 10.1145/2494091.2494120 · DOI
- 10.1145/2800835.2800901 · DOI
- 10.1109/TBME.2014.2306773 · DOI
- 10.1007/978-3-319-07230-2_30 · DOI
- 10.1145/2582051.2582066 · DOI
- anderson, 2000, Home or hospital for stroke rehabilitation? results of a randomized controlled trial i Health outcomes at 6 months, 31, 1024
- 10.1049/cp:20080645 · DOI
- mattern, 2003, From smart devices to smart everyday objects, Proceedings of Smart Objects Conference, 15
- 10.1080/10807030490513793 · DOI
- 10.1007/978-0-387-34747-9_18 · DOI
- timmermans, 2009, Arm and hand skills training preferences after stroke, 31, 1344
- 10.1109/ICDM.2003.1250975 · DOI
- kim, 2012, Comparing image classification methods: K-nearest-neighbor and support-vector-machines, Ann Arbor, 1001, 109
Cited by
Sorted by citations
Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review
Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., & McDonald-Maier, K. D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103197 · Cited by 180
“Activity recognition Are systems which aim to identify specific movements of rehabilitation of the patients and differentiate between them for record and monitoring purposes [41–51], in this category researchers monitored Activities of Daily Living (ADL) [75] and they most frequently covered detecting general activiti…”
BackgroundMethodResultInfluential
Metadata
- Authors
- Bobin, M., Amroun, H., Boukalle, M., Anastassova, M., & Ammi, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Venue
- 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 189-195
- OpenAlex
- W2948666881
- Cited by
- 9
- References
- 1
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 2 · 2020: 1 · 2018: 1
- FWCI
- 0.30
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Keywords
- stroke patients, home monitoring, activity recognition, embedded sensors, sedentary behavior, activities of daily living, support vector machine, discrete cosine transform features, multilayer perceptron, smart objects
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Smart Cup to Monitor Stroke Patients Activities During Everyday Life. https://app.cochat.ai/papers/10.1109/cybermatics_2018.2018.00062
- The paper
- Bobin, M., Amroun, H., Boukalle, M., Anastassova, M., & Ammi, M. (2018). Smart Cup to Monitor Stroke Patients Activities During Everyday Life. 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). https://doi.org/10.1109/cybermatics_2018.2018.00062
