# Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study
## 2 Related Works  
This section presents a summary of the most recent HAR techniques on embedded equipment.  
In [12], a machine learning (ML) and data fusion technique applied to physical activity classification using inertial measurement unit signals is adopted with an accuracy of classification of 96.42%, and the algorithm is implemented both on Intel i7 CPU and NVIDIA 1080 GPU. The authors in [13] used an accelerometer-based method for human activity recognition and implemented their work on the CPU with an accuracy rate of 78.00%. A biometric user identification based on human activity recognition using wearable sensors is investigated in [14]. The framework uses deep learning models and is implemented on: (1) Intel i5-8400 CPU with an Accuracy of 91.23% and (2) NVIDIA RTX2070 GPU with an Accuracy of 85.57%.  
The deployment of deep neural networks on microcontrollers is adopted in [15]. A CNN-based approach is implemented on Nucleo-L452 RE-P and SparkFun Edge board (ARM Cortex-M4F) with an accuracy rate of 92.46%. Also, a Recurrent Neural Network is used for human activity recognition in STM32L476 RG microcontroller (ARM Cortex-M4F) using accelerometer data with an accuracy rate of 95.54%. In [16], a deep residual bidir-LSTM for human activity recognition using wearable sensors is developed and implemented on NVIDIA GTX 960M GPU with an accuracy of classification of 93.54%. A lightweight deep learning model for human activity recognition on edge devices is investigated and implemented on Raspberry Pi in [17] with 95.78% accuracy. Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition in [18]. Table 1 summarizes the state-of-the-art HAR methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Algorithm** & **Performance** & **Hardware Architecture** \\ \hline
[12] & ML \& data fusion & 96.42\% &
\begin{tabular}{c} Intel i7 CPU and \\ NVIDIA 1080 GPU \\ \end{tabular} \\ \hline
[13] &
\begin{tabular}{c} accelerometers \\ based method \\ \end{tabular} & 78.00\% & CPU \\ \hline
[14] & \
\begin{tabular}{c} biometric user \\ identification \\ \end{tabular} & 91.23\% &
\begin{tabular}{c} Intel i5-8400 CPU \\ NVIDIA RTX2070 GPU \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: State of the art HAR techniques on embedded equipment: a performance study.  
The authors in [20] used HAR and embedded applications based on the Convolutional Neural Network. A stochastic gradient descent algorithm is used to optimize the parameters of the CNN and the trained network model is compressed on STM32 CubeMX-AI. In [21], a HAR using a machine learning technique in a low-resource embedded system is investigated. The authors present a prototype of a wearable device that identifies a person's activity: walking, running, or staying still. The system consists of a Texas Instruments MSP-EXP430G2 ET launchpad, connected to a BOOSTXL-SENSORS booster pack with a BMI160 accelerometer. A Recurrent Neural Network for HAR in Embedded Systems Using PPG and Accelerometer Data is developed in [19]. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low-cost, low-power microcontroller, ensuring the required performance in terms of accuracy and low complexity. [22] introduced a novel approach to HAR by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is affixed to the subject's chest to capture their movements. Most existing methods in the literature for activity recognition are based on accelerometer wearable sensors. They demonstrate that it is possible to execute HAR AI algorithms on constrained computers. The novelty in this work consists of implementing a non-based AI activity recognition algorithm based on home automation sensor data on embedded equipment.