# New Eldercare Robot with Path-Planning and Fall-Detection Capabilities
## 6 Conclusions and Future Work  
In this paper, an eldercare robot was developed to address the unique needs of the elderly population. The comprehensive approach included hardware design, motor calculations, system integration, and experimental testing. The robot's control architecture accommodates both hardware and software layers, utilizing Hector SLAM and local path planners within the ROS framework. This enables the robot to navigate, detect obstacles, and provide fall detection, showcasing its potential for enhancing elderly well-being. RRT* was chosen as the path-planning technique in experimental testing. The robot was tested in a real indoor environment and achieved a positioning accuracy of 94.7% and 93% in the x- and y-axes, respectively. Additionally, a fall-detection model was developed using the YOLOv7 algorithm which achieved an accuracy of 96% and a precision of 99.1% after testing. The model size was reduced by 40% through quantization and an additional 20% through pruning to enable its deployment in the Jetson Nano controller.  
However, this study acknowledges that this is an initial step, and further validation and real-world testing are needed across caregiving settings. Continuous refinement of both hardware and software components is crucial to address evolving elderly needs. Future exploration could involve enhancing human-robot interactions through incorporating natural language processing and emotion recognition. Also, advanced fall detection can be achieved through integrating more sensors for real-time and accurate results. Additional features can be used to classify falls and generate datasets that can be segmented according to different features, such as the human body, head pose, joint angles, gait, background, lighting, and any noise or distortion. Finally, an automatic docking station is planned to be added to the robot.  
Conceptualization, Y.E.-S.; methodology, A.A. and Y.E.-S.; software, A.E. and A.A.; formal analysis, A.A.; investigation, A.E. and Y.E.-S.; writing--original draft, A.A.; visualization, A.E.; supervision, A.A. and Y.E.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Publicly available dataset was analyzed in this study. This data can be found here: [[https://www.kaggle.com/datasets/elwalyahmad/fall-detection](https://www.kaggle.com/datasets/elwalyahmad/fall-detection)]([https://www.kaggle.com/datasets/elwalyahmad/fall-detection](https://www.kaggle.com/datasets/elwalyahmad/fall-detection)) (accessed on 28 February 2024).  
The authors declare that there is no conflict of interest regarding this manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Mechanical Design** & \multirow{2}{*}{**Sensors**} & **Indoor Navigation Capability (Y/N)** & **Fall Detection Technique (Y/N)** \\ \hline \multirow{4}{*}{Giraff [10]} & \multirow{2}{*}{Differential} & 2D Lidar, RGB camera, Fisheye camera & Y (Teleoperated with obstacle avoidance capability) & N \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \multirow{2}{*}{Zora [14, 15]} & Legged humanoid robot & Bumper, IMU, Sonars, Camera & N & Y (Pose estimation) \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \multirow{2}{*}{The new eldercare robot} & Differential mobile robot & 2D Lidar, IMU, CSI camera & Y (Hector SLAM and RRT*) & Y (Object detection using YOLOv7) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._