### 2.1 Internet of Things

The Internet of Things is an important part of modern intelligent systems and plays an important role in the development of information flow. Integrated detection, storage, computer technology and its applications and design concepts of the Internet of Things play an important role in industry and commerce, processing and everyday life [4]. The object-based Internet platform, with the support of information technology, establishes a wide range of connections between objects, forming an interactive three-dimensional network, connecting the virtual world and the physical world.

The basic characteristics of the Internet of Things can be summarized in the following three points: (1) full perception: use radio frequency identification devices, sensors and QR codes to receive information on objects anytime and anywhere, (2) reliable transmission: real-time and accurate transmission of information on objects through the network and (3) intelligent processing: use intelligent computer technology to analyze and process a large amount of data and information and then check intelligent objects [5, 6].

### 2.2 Motion capture technology

Motion capture technology uses video equipment, motion sensors and other equipment to monitor the movement of some or all of the joints of the human or animal body, measure joint motion information and provide reference data for gait recognition and film production, and television [7]. This technology currently has a wide range of applications in film and television production, interactive games, virtual reality and personnel training.

Kalman filter is an unbiased, linear and minimum variance optimal estimation theory [8]. Understanding the mathematical model of the state vector and the observation vector, the characteristics of the statistical noise of the state and the means of observation and the initial value of the state of the system, the measured data and the sensor state equation can be used to derive the relationship between the system state medium and the observation data. Kalman filtering is divided into two stages: prediction and information [9, 10]. In the prediction phase, the state estimation at each moment is estimated based on the previous state value. So get the prediction equation:

$$P_{x,x - 1} = Q_{x,x - 1} P_{x - 1} + H_{x} I_{x - 1}$$

(1)

That is, the state at each moment is transformed from the state at the previous moment through the state transformation matrix, plus the control amount at the current moment [11]. Since this system does not contain the control quantity at every moment, it can be simplified as:

$$P_{x,x - 1} = Q_{x,x - 1} P_{x - 1}$$

(2)

In addition to the estimation of the system state at each moment, it is also necessary to estimate the accuracy of its estimation, so it is expressed in the form of covariance in formula (3):

$$A_{x,x - 1} = Q_{x,x - 1} O_{x - 1} Q_{x,x - 1}^{T} + B_{x - 1}$$

(3)

In this way, the next state can be predicted based on the previous state. Since Kalman filtering is an iterative process, the state needs to be updated after each prediction [12].

After obtaining the current state estimation value, the current measurement value can be combined to obtain an optimal estimation value of the current state.

$$P_{x} = P_{x,x - 1} + K_{x} [C_{x} - H_{x} P_{x,x - 1} ]$$

(4)

Among them, \(C_{x}\) is the observed value, and \(K_{x}\) is the Kalman gain, which can be obtained by formula (5):

$$K_{x} = A_{x,x - 1} + H_{x}^{T} [H_{x} A_{x,x - 1} H_{x}^{T} + R_{x} ]^{ - 1}$$

(5)

Finally, find a new covariance matrix to complete this iteration.

$$A_{x} = [1 - K_{x} H_{x} ]A_{x,x - 1} [1 - K_{x} H_{x} ] + K_{x} R_{x} K_{x}^{T}$$

(6)

### 2.3 Capture method of wireless inertial sensor

The angle between the device and the gyro can be obtained by reading the direction pointed by the axis by the method; that is, the angular velocity can be obtained. The magnetometer is used to test the strength and direction of the magnetic field [13]. The nine-axis inertial measurement sensors currently used for motion signal collection including three-axis gyroscope sensors, three-axis acceleration sensors and three-axis magnetic induction sensors are relatively mature, which can better realize the position and positioning of the sensor nodes of human body motion capture, thereby realizing actions capture accurately.

The technology is divided into two parts: front-end hardware and back-end software. The main functions and contents of raw materials include the use of motion capture sensors for the collection of human motion data and the transmission of this data to computer motion data [14, 15]. The main features and content of the back-end software use a computer to efficiently process the collected traffic data, so that the computer can automatically recognize the activity category of the captured object and use the computer to reproduce the action and human interaction.