# Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition
## 6 Conclusions  
A feasible detection system for ADL is beneficial for practitioners, especially for health care and safety applications designed to protect seniors from falling. This research develops a NN-based system to detect the ten most common types of ADL including standing, bending, squatting, sitting, eating, raising one hand, raising two hands, sitting plus drinking, standing plus drinking, and falling. The proposed system starts with ADL image collection and processing using the sliding window concept, followed by the skeletal recognition technique for identification of ADL. Four types of most common NN methods are compared to determine which one is optimal for ADL recognition. The results show the DNN-based system to have the optimal outcome of a 95% accuracy rate in comparison to the single layer BPNN, multi-layer BPNN, and CNN systems. The empirical evaluation using lengthier footage containing all types of ADL randomly mixed together yields a high average accuracy rate of 95.1%. The proposed system even performs perfectly with a 100% accuracy rate for five types of ADL: standing, raising one hand, raising two hands rising, sitting plus drinking, and standing plus drinking.  
This study describes a novel mechanism suitable for both practical and academic applications. Integrating the easily accessible ADL images, data processing, and noise filtering concepts with DNN method, we produce a camera-ready system for practical use. It has a high successful identification rate >95% for the recognition of most common ADL, especially for the detection of falling at 97%. The system does not require high-end equipment to obtain footage for recognition and can easily process most common ADL. It is affordable and efficient enough to benefit the users. Some suggestions for follow-up studies could include in more efficient algorithms that yield higher accuracy rates and more detailed posture recognition. The evaluation was conducted using a 5-min footage containing all 10 types of the most common ADL. Although each type of ADL appeared randomly 3 to 5 times in the footage, a larger data set is needed for further evaluation of the method such as lengthier videos and numerous videos with more complex or mixed ADLs. Future studies may also focus on improving the relatively poor recognition rates for postures such as bending and squatting either by providing higher-resolution video or developing more advanced algorithms.  
Conceptualization M.S.; Methodology, M.S. and S.T.; Validation, D.W.H. and J.C.; Resources, M.S. and S.T.; Writing--original draft preparation, D.W.H. and J.C.; writing--review and editing, J.C. and H.W. All authors have read and agreed to the published version of the manuscript.  
This paper was partly supported by the Ministry of Science and Technology (MOST), Taiwan, for promoting academic excellent of universities under grant numbers of [MOST 109-2622-E-008-018-CC2]; [MOST 108-2221-E-008-002-MY3]; [MOST 109-2221-E-008-059-MY3]; [MOST 107-2221-E-008-084-MY2]; [MOST 109-2634-F-008-007]; [MOST 108-2634-F-008-001].  
The authors declare no conflict of interest.