## 5 Conclusions and Future Work  
This study demonstrates the feasibility of using a machine learning model to identify emotions in children with severe motor and communication impairments (SMCIs) by analyzing their physiological signals. By adapting a model developed for typically developing participants from the DEAP dataset, we created a version suitable for SMCI participants using the BDAT dataset. Key adaptations, such as removing the respiratory signal and training the model on shorter duration segments, allowed for comparable accuracy levels to be identified. The model performed better than chance, suggesting the potential for future refinement and application in healthcare settings.  
Future work should focus on expanding the dataset for SMCI participants, integrating additional signals like ECG for improved accuracy, and developing personalized models to account for individual variations. Incorporating the dominance dimension and recognizing a broader range of emotions could further enhance the model's utility. This research highlights the potential of machine learning to improve the understanding of and supporting the emotional well-being of children with complex communication needs.  
Data that include children with SMCIs are required to better understand how their physiological signals may be the same or differ from the typically developing population. Some research has indicated that heart, skin, and lung activity differ in populations with diseases, but the specific physiological differences have not been described relative to the various populations of persons with disabilities. This severely limits the knowledge base about how these sensors function for populations of participants with neurological disabilities, such as SMCIs.  
Future studies could also introduce the collection of data from different placements of sensors to identify whether that too affects the data relative to typical developing populations. If the GSR could be optimized for other locations on the body, there may be opportunities for the collection of longer-term data. Placing the sensors on the hand creates difficulties, especially for persons with spastic movements or discomfort. Though ECG is currently not used often in daily applications for collecting HR data, with the newer models of watches these data will be more accessible in the future and should be used for assessing emotion.  
The development of a device that could detect the emotions of children with SMCIs and then provide that information to an unfamiliar person, such as a doctor or nurse or even a friend at school, could greatly enhance the responses of these individuals and allow them to better cater to the needs of the child.  
Conceptualization, C.V. and T.C.D.; methodology, T.C.D.; software, C.V. and K.P.; validation, C.V.; formal analysis, C.V.; data curation, C.V. writing--original draft preparation, C.V. and K.P.; writing--review and editing, T.C.D.; supervision, T.C.D.; project administration, C.V. and T.C.D.; funding acquisition, T.C.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a Discovery Grant from the Natural Science and Engineering Research Council of Canada [NSERC RGPIN 2016-04669 and RGPIN 2023-05354] and a CREATE grant [497303-2017].  
This study was conducted in accordance with the Declaration of Helsinki and approved by Queen's University Health Sciences and Affiliated Teaching Hospitals' Research Ethics Board (HSREB) on 24 August 2022 (MECH-76-22).  
Informed Consent Statement: Informed consent was obtained from the parents of the participants involved in this study while the participants provided their informed assent.  
All the data from the development of the DEAP model are available online [16]. Permission was obtained to collect physiological data from parents and participants for the BDAT dataset, but not to share them publicly.  
We would like to acknowledge the support of and data collection assistance from [PERSON] and [PERSON].  
The authors declare no conflicts of interest.