# Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network
## 6 Conclusions  
This paper presented a 2D CNN model for recognizing and predicting simple human activities with the help of an existing dataset based on accelerometer readings. The proposed model identifies and predicts the majority of the six activities with a high degree of accuracy--over 85% of the time, on average. The approach of building the convolutional neural network in a two-dimensional format and its application in HAR is the highlighted feature of our work. This paper also shows how important research on human activity recognition has become in present times, with newer methods coming over time.  
In addition, this paper also shows how learning evaluation is necessary before proceeding towards model development. This helps in comparing the learning scores with the actual prediction scores of the model, and thereby a consistency check can be performed. Moreover, the results obtained in this paper were comparable with a renowned state-of-the-art method--the LSTM.  
Our future work can focus on working on newly developed datasets that are more aligned with real-life scenarios, consisting of a greater number of activities to predict and a bigger number of participants. Otherwise, an effort can also be made to collect data, per se, with a greater number of participants with different lifestyles, behaviour, ageing, sex, etc. Another future work could include abnormal human behaviour prediction. With the help of a functional activity recognition system, one can supervise the dependent persons such as the elderly people in smart homes and evaluate their activity level for healthcare services. Moreover, for all the residents staying in smart buildings, human activity recognition can be utilized in checking their comfort level concerning factors such as temperature and humidity. Last but not least, 1D and 3D approaches towards building the CNN model could be tried out for predicting human activities in future.  
All authors have contributed equally to this paper. Conceptualization, A.P.; Methodology, A.P.; Supervision, A.K.T., M.M.A., A.A., R.F.M. and A.M.M.; Writing--original draft, A.P.; Writing--review & editing, A.P. All authors have read and agreed to the published version of the manuscript.  
We deeply acknowledge Taif University for supporting this research through Taif University Researchers Supporting Project Number (TURSP-2020/328), Taif University, Taif, Saudi Arabia.  
Not applicable.  
The WISDM (Wireless Sensor Data Mining) data set used for this research work was taken from [[https://www.cis.fordham.edu/wisdm/dataset.php](https://www.cis.fordham.edu/wisdm/dataset.php)]([https://www.cis.fordham.edu/wisdm/dataset.php](https://www.cis.fordham.edu/wisdm/dataset.php)).  
This research work gratefully acknowledges the Centre for Advanced Data Science, VIT Chennai for conducting a research internship program in the month of July 2021 (under the supervision of [PERSON]) and supporting the research activity, without which it would not have been possible to complete this research paper. And we deeply acknowledge Taif University for supporting this research through Taif University Researchers Supporting Project Number (TURSP-2020/328), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activity** & **LSTM Accuracy (\%)** & **2D CNN Accuracy (\%)** \\ \hline Downstairs & 86\% & 89\% \\ Jogging & 96\% & 94\% \\ Sitting & 92\% & 100\% \\ Standing & 94\% & 100\% \\ Upstairs & 87\% & 61\% \\ Walking & 99\% & 94\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of accuracy between 2D-CNN and LSTM.