# Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices
## 2 Related Work
Moving downstairs activity obtained an 89.00% recognition rate and moving upstairs a 91.00% recognition rate. The algorithm was unable to obtain a very good recognition rate for both of these activities, probably due to the similarities between the signals. The labeling of the activity data set is the most important part of improving activity recognition performance according to [26]. This research proposes a system based on user feedback regarding the recognized activity. The user was informed through a notification. The recognition algorithm used is a long short-term memory (LSTM) model which was trained using an open dataset. The accelerometer data that are read from the smartphone are recognized by the trained algorithm and the result is sent to the user via a notification. The user approves the newly labeled data and, in this way, the raw data set increases in size. This implementation improves the recognition rate, according to this research, with 10% more than the classical method with the same model. For the kNN model, the recognition rate was improved by 13.40%, and for LSTM, by 16.20%. The concept of the Internet of Things can help improve people's life nowadays, by handling and maximizing the data collected from multiple sensors. The authors of [27] propose a human activity recognition real-time system based on data collected from smartphones and smartwatches. The real-time data are collected from the accelerometer and gyroscope sensors. The watch is placed on the wrist and the smartphone is in a pocket. For the accelerometer, the signals are separated into body and gravity components. The data are segmented into overlapped windows. After the new features are extracted, the data scaling factors and normalization are applied. Four types of algorithms were tested for human activity recognition: Random Forest (RF), Multi-layer Perception (MLP), Support Vector Machines (SVM), and Naive Bayes (NB). The training was implemented with a dataset collected by researchers. The best recognition rate was obtained by the RF model at 98.72%. Also, this method was tested with the WISDM dataset, and a 98.56% recognition rate was obtained. Using wearable sensors built into everyday objects is common practice, even reaching the level of the incorporation of stretch sensors into clothes. Combining stretch sensors and inertial sensors can yield good results with an accuracy rate of 97.51% [28]. This approach implies that the user wears custom add-on modules that can also be built into the clothes, and it requires specialized hardware modules for this, making the system a bit more difficult to wear for extensive periods of time. Options to unobtrusively monitor a user's activity can also be used, without the subject carrying a dedicated device; for example, the authors of reference [29] highlight an innovative system that uses indoor WiFi signals to detect users' activity by the different patterns generated. This system can achieve a good recognition rate; the CNN-ABiLSTM model reached an accuracy of 98.54% but it has some drawbacks as it can be used only in a specific setup environment.