# Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices
## 6 Conclusions  
Our main objective was to implement a real-time cloud-based human activity recognition system that uses image classification at its core. The system should be able to expose HAR capabilities from the cloud or locally via rest API calls. In order to achieve this, a.NET core C#-based web API was implemented to expose the activity recognition functionality. The main HAR functionality was achieved using a deep neural network created based on a pre-trained TensorFlow Resnet50 DNN model and trained for HAR using plot images. The created system was trained using data from the WISDM dataset, which is open access. The training data were generated using a separate.NET core C# application that generated the plot images based on raw accelerometer and gyroscope data. Since the proposed system can also be deployed to the cloud, it can be easily expanded to support multiple sensor modules and users at the same time based on its rest implementation. We managed to obtain a decent human activity recognition rate of 94.89%, using the following activities subset: Walking, Jogging, Stairs, Sitting, Standing, Typing, and Brushing Teeth, demonstrating that a real-time HAR system based on plot image recognition and REST requests can be a good system architecture for a real-time activity recognition system. We managed to improve the previously obtained human activity recognition accuracy of 93.10%, as shown in [31], to 94.89%, obtaining an increase of 1.79%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & & & & & & \\
**Number** & **Reference** & **Year** & **Algorithm** & **Accuracy** & **Recognised** & **Data Source** & **Type of Data** \\  & & & & **\%** & **Activities** & **Sensors** & **Representation** \\ \hline
11 & [23] & 2020 & LSTM & 89.60 & \
\begin{tabular}{c} Eighteen \\ activities 2 \\ \end{tabular} &
\begin{tabular}{c} Accelerometer, \\ Gyroscope \\ \end{tabular} & Raw Data Windowing \\
12 & [23] & 2020 & Hybrid-CNN-LSTM & 96.20 & \
\begin{tabular}{c} Eighteen \\ activities 2 \\ \end{tabular} &
\begin{tabular}{c} Accelerometer, \\ Gyroscope \\ \end{tabular} & Raw Data Windowing \\  & & & & & & \\
13 & [24] & 2020 & kNN HAR-SR & 77.82 & Six activities 3 & Accelerometer & Symbolic representation \\
14 & [25] & 2022 & Matching Filter CNN & 97.32 & Five activities 4 & Accelerometer & Matching filter over \\  & & & & & & Raw Data \\
15 & [26] & 2019 & Labeled CART & 77.30 & Six activities 3 & Accelerometer & Labels over Raw Data \\
16 & [26] & 2019 & Labeled kNN & 80.01 & Six activities 3 & Accelerometer & Labels over Raw Data \\
17 & [26] & 2019 & Labeled LDA & 76.60 & Six activities 3 & Accelerometer & Labels over Raw Data \\
18 & [26] & 2019 & Labeled LR & 77.80 & Six activities 3 & Accelerometer & Labels over Raw Data \\
19 & [26] & 2019 & Labeled LSTM & 78.30 & Six activities 3 & Accelerometer & Labels over Raw Data \\
20 & [26] & 2019 & Labeled RF & 81.50 & Six activities 3 & Accelerometer & Labels over Raw Data \\
21 & [26] & 2019 & Labeled SVM & 77.50 & Six activities 3 & Accelerometer & Labels over Raw Data \\
22 & [27] & 2022 & RandomForest & 98.56 & Thirteen & New features over Raw \\  & & & & & & \\ \hline \hline \end{tabular}  
* 1 Jogging, moving downstairs, walking, moving upstairs, standing, sitting. 2 Kicking, stairs, standing, jogging, sitting, walking, sandwiching, chips, drinking, soup, pasta, folding, typing, teeth, catching, clapping, dirbbling, and writing. 3 Walking, running, moving downstairs, moving upstairs, sitting, standing. 4 Walking, jogging, moving downstairs, moving upstairs, sitting. 5 Riking, having coffee, walking downstairs, eating, jogging, sitting, smoking, standing, giving a talk, typing, walking upstairs, walking, writing.  
\end{table} 
Table 6: _Cont._Since only raw accelerometer and gyroscope data are used by the proposed HAR platform, we should be able to expand the usable sensor modules to any sensor module that has an accelerometer, gyroscope, and network capabilities.