# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 3 Related Work  
There is a vast amount of literature on using inertial sensors for position and orientation estimation, addressing various application fields and the challenges associated with estimating and parameterizing orientation [20]. The accuracy of these predictions depends  
Figure 1: IMU fusion. on the choice of the prediction model and the algorithm employed [21]. Improving accuracy remains a significant research challenge. Researchers have gathered diverse experiences in building human motion tracking models based on Inertial Measurement Units (IMUs). These include introducing new sensing techniques, positioning sensors at different body parts, and combining them with wireless communication technologies. [PERSON] et al. [22] examined the strengths and weaknesses of approaches based on IMUs and multi-view imagery. Supporting their findings, [PERSON] et al. [23] demonstrated that it is possible to identify six fundamental human activities using a simple sensor. [PERSON] et al. [24] collected smartphone activity identification data and applied a hidden Markov model to classify the six typical human activities. [PERSON] et al. [25] developed an online physical activity recognition system using wireless wearable sensors that can be recharged through body energy harvesting. This recognition was achieved using a Random Forest (RF) algorithm. [PERSON] et al. [26] conducted an in-depth investigation into using sensors for healthcare, examining all aspects related to the design of human activity recognition (HAR) systems. They analyzed different types of wearables, environmental sensors, and hybrid sensors, highlighting their strengths and weaknesses. Additionally, they elaborated on data preprocessing, feature learning, and classification techniques. In particular, they focused on manual and automatic feature extraction creation. Several applications have been developed with interesting methodological approaches. [PERSON] et al. [27] proposed a new method for recognizing human activities using an attention mechanism with multiple IMU sensors placed at various body locations. The captured signals were preprocessed in the frequency domain, and the most discriminating features were extracted. They introduced a sensor attention mechanism to determine the importance of specific sensors integrated into a trainable deep neural network (DNN) layer. A focus-based casting module was utilized to represent the features precisely. Five public datasets covering daily activities, sports, and automotive maintenance tasks were used to evaluate their model. The results demonstrate superior performance compared to state-of-the-art methods. [PERSON] [28] developed a model to correct drift and attitude angle errors in MEMS sensors and the Zigbee network for capturing human movement. He employed a Kalman filter algorithm to compensate for errors, integrating information from the gyroscope, accelerometer, and magnetometer. To enhance tracking accuracy, he applied an algorithm based on sensor sensitivity and another that considered the connections and limitations of human joints. Testing involved sensor data from the wrists, elbows, and shoulders of five subjects across 40 postures. The average essential tracking accuracy was 0.075 m, which improved by 15% with advanced algorithms. Additionally, a backpropagation (BP) neural network was used for the motion identification module, aiming to create a human recognition system on the Android platform to detect falls and generate early alarms, resulting in a more efficient and accurate model than others. [PERSON] et al. [29] studied the performance of wearable Inertial Measurement Unit (IMU) sensors and vital signs for human activity recognition (HAR), specifically in patients with motor disorders. They employed a feature selection (FS) technique [30] to extract the most relevant features from the PAMAP2 public dataset [31], which includes data from three IMUs, a heart rate sensor, and a temperature sensor. Measurements were recorded from nine subjects engaged in 12 different activities.