2.1 Movement pattern recognition
Locomotion mode recognition plays a key role in the control of the system mechanism in different motion mode scenes [4]. The range of sports modes is very wide. Sitting, standing, walking, running, up and down stairs, jumping, falling, etc., are all different sports modes. Many documents have more descriptions of sports modes, including walking, running, sitting, and standing. Movement patterns including jumping and jumping are recognized, and movement patterns including up and down stairs, up and down elevators are analyzed and recognized, and sports including cycling, driving, and housework are involved [5]. Different sports modes have different characteristics. In order to recognize different sports modes, certain data support is needed. Recognition needs to start from the data collected by the sensor, after data preprocessing, feature extraction, feature transformation and selection, classification, and recognition; the recognition result of the movement pattern is obtained. The general recognition process of motion patterns is shown in Fig. 1.
There are many ways to choose the sensor for the first step, including video image acquisition and non-video image sensor. As a noninvasive acquisition method, video image recognition motion mode has been applied in many fields, and its research is also relatively comprehensive [6]. However, non-video image sensors are gradually being used to recognize human movement patterns due to their small size, lightweight, and low cost. Such sensors are generally arranged in various parts of the body, and one or more sensors are fixed on the waist, arms, abdomen, lower limbs, etc., or directly use smart phones with inertial measurement units to recognize movement patterns [7]. The selection of the measurement unit varies according to the type of application, generally including accelerometers, gyroscopes, magnetometers, heart rate sensors, skin conductance sensors, wireless locators, etc., for data collection [8]. Aiming at the time mismatch problem, the article uses a three-axis acceleration sensor. The advantage of the three-axis acceleration sensor is that when the direction of movement of the object is not known in advance, only the three-dimensional acceleration sensor can be used to detect the acceleration signal. Most studies use acceleration data as the source of motion information and use single-axis or three-axis acceleration data to identify motion patterns.
Compared with other sensors, although the measurement of the accelerometer is more sensitive and the measurement contains high-frequency components, its characteristics of small relative error, simple offset calculation, small temperature drift, and less environmental interference make it suitable for obtaining sports information [9]. The application of multi-sensor technology in rock climbing requires high reliability and stability of video equipment. Accelerometer is a sensor used to test linear acceleration. Compared with electronic gyroscopes, it has the characteristics of long-term stability. By analyzing the dynamic acceleration, the way the device moves can be analyzed. A gyroscope is a device that measures the angular velocity of rotation. A gyroscope is placed on the human body to measure the angular rotation of different parts, provide human body motion information, and provide data for the recognition of motion patterns. The measurement error of the gyroscope is relatively high, there are bias and drift conditions, and it is not suitable for independent use. Generally, the magnetometer data and the gyroscope data are used after data fusion [10]. Heart rate sensors and skin surface conductance sensors are sensors that measure biological characteristics. People's heart rhythm performance and EMG signal characteristics in different sleep, walking, sitting, and other motion states are different. Different motion patterns can be studied by measuring heartbeat data and surface electromechanical signals. Measure the changing law of the data to identify the movement pattern of the human body [11]. The basic movements of rock climbing are mainly around arms, feet, etc., and there are many fulcrums on the rock wall. According to different positions and different angles, they can be pulled, pinched, and climbed. These movements determine the center of gravity of the human body. As a positioning device, a wireless locator includes two parts: a base station and a positioning tag. It judges the location of the person according to the characteristics of the signal received by the tag and indirectly infers the movement pattern of the human body based on the position change information. This method is subject to the limitations of the venue and can only be applied within a certain range [12].
2.2 Multi-information fusion sensor
The so-called multi-information fusion is actually the data information collected from multiple sensors or other sources through computer calculation and comprehensive analysis and processing under certain specific specifications to complete the subsequent information processing process [13]. It collects the data of the observation target through N different types of sensors, performs feature extraction and transformation on the output data of the sensors, and classifies them after attribute judgment. This is the same as the way the human brain thinks about problems, especially in making full use of different time. With the multi-sensor data resource of space, computer technology is used to obtain multi-sensor observation data in time series. The basic principle of multi-information fusion sensors is very similar to the process of human brain processing information. Each sensor is optimized and processed through multifaceted and multi-angle information fusion, and finally a unified description of the required observation environment is obtained. On this journey, we need to make full use of the multi-directional information resource data obtained by more sensors and process and use them reasonably [14]. This not only uses the advantages of multiple sensors working at the same time, but also comprehensively processes the information data obtained from other places to make the multi-fusion information sensor system more intelligent [15]. Multi-information fusion sensor system has four obvious advantages: information redundancy, information complementarity, timeliness of information processing, and low cost of information processing. The typical structure of the post-fusion algorithm is shown in Fig. 2.
With the development of information fusion technology and the wide application of multi-sensor information fusion systems, the problem of time registration in information fusion has gradually attracted people's attention. In the actual multi-sensor system, due to the different working tasks of each sensor, the performance of the sensor is different in the environment [16]. Even if the same target is observed, the observation data of different sensors are not necessarily synchronized. Therefore, the measurement data cannot be directly fused. It is necessary to convert the target observation data obtained by different sensors at different times to a unified fusion moment, that is, in time, as shown in Fig. 3.
2.3 Rock climbing
Rock climbing is the use of technical equipment and companion protection for climbers to perform thrilling actions such as turning, jumping, and pulling up on rock walls of different heights and difficulties, relying on tenacious will, strong physical strength and flexible thinking ability. Complete the climbing of the entire route [17].
In traditional climbing, climbers set up protection points along the road by themselves, and the goal is to finish climbing. Because there is no permanent protection point along the road, traditional climbing usually climbs along the fissure. It can be divided into artificial climbing and free climbing [18]. Free climbing only uses hands, feet, and natural handles to climb; ropes and other artificial equipment are only used to ensure that they are not helpful for climbing. Manual climbing is an extra method of climbing on the rock wall with ropes and other artificial equipment, adding artificial grip or stepping points, or any form of assistance, to climb high. Manual climbing requires the use of artificial tools to climb during the climbing process, such as handheld rope ladders, fixed points, protection points, such as ascenders, rope ladders, rock nails, and rock hammers. But because the rock nails will cause damage to the rock wall, in the environmental protection now, generally only the rock wedges are used as fixed points or guarantee points [19]. Participation in free climbing: This term is relative to artificial climbing. When climbing, only the limbs of the body are used to track the natural handle points or foot points. Traditional equipment is only used to set up protection points, not for climbing. The rope is only used to ensure safety. The traditional climbing posture is shown in Fig. 4.
The difference between sport climbing and traditional climbing is that the climbing route has preset protection points, and climbers do not need to place protection points by themselves [20]. Since the development of rock climbing, due to the advancement of technology and equipment and the purpose of popularizing rock climbing, sport climbing has become the mainstream climbing method in the rock climbing industry due to factors such as safety and easy entry. From the early stage of rock climbing to the later stage, some rock climbers gradually leave the alpine rock field and become a new sport. Climbers climb rock fields that have permanent protection points, such as artificial and natural rock fields [21]. The goal of climbers is not just to climb to the top, but to challenge more difficult routes. The sport climbing posture is shown in Fig. 5.
2.4 Calculation and selection of feature components
In order to increase the correct rate of recognition of different motion patterns during exercise, consider selecting an appropriate time point in a gait cycle, and intercept the data of each acceleration sensor with a fixed window length, which is used to identify the motion pattern of the person [22]. This method is different from the sliding window to identify the data in the entire time range. According to this interception method, the characteristics of the data obtained are more obvious and conform to the gait law [23]. Although it is necessary to perform multiple classification recognition in a gait cycle, the overall recognition rate is higher than that in the whole cycle.
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(1)
Relief feature selection algorithm
Relief feature selection method is a filtering feature selection method that first selects features and then performs training and learning [24]. For a second-class problem, the Relief feature selection method sorts according to the importance of each feature and selects the features with high importance to participate in the learning of the classifier, and the evaluation of the importance is achieved through relevant statistics. Suppose the training set S is
$$S = \{ (x_{1} y_{1} ),(x_{2} y_{2} ), \cdots (x_{n} y_{n} )\}$$
(1)
The training set S contains only two categories of data. For each sample \(x_{i}\), define its guessed neighbor \(x_{i,nh}\) and wrong guessed neighbor \(x_{i,m}\). Guessed neighbor \(x_{i,nh}\) is to find the closest sample in the same type of data set, namely:
$$x_{i,nh} = \arg \min \left| {x_{i} } \right. - \left. x \right|_{2}$$
(2)
Guessing the wrong neighbor \(x_{i,nh}\) is to find the nearest sample among samples of different categories from sample \(x_{i}\), namely:
$$x_{i,nh} = \arg \min \left| {x_{i} } \right. - \left. {x^{n} } \right|_{2}$$
(3)
Then, the importance of the jth component in each sample vector is defined as
$$\sigma^{j} = - \sum\limits_{i} {\left| {x_{i} } \right.}^{j} - \left. {x_{i,nh}^{j} } \right| + \sum\limits_{i} {\left| {x_{i} } \right.}^{j} - \left. {x_{i,nm}^{j} } \right|$$
(4)
where \(x_{i}^{j}\) represents the jth component in the sample \(x_{i}\) vector and is normalized:
$$x_{i}^{j} = \sum\limits_{k = 1} {\left| {x_{i} } \right.} (x_{k}^{j} )^{ - 1}$$
(5)
It can be seen from the above formula that for the jth component, if the distance between x and the guessed neighbor on the j component is less than the distance of the guessed neighbor, then the jet component is beneficial to classification and its importance should be increased. On the contrary, if the distance between x and the guessed neighbor on the j component is greater than the distance of the guessed neighbor, the jth component is not conducive to classification, and its importance should be reduced [25]. Finally, the importance of each sample on the component is calculated, and it is concluded that the larger the value, the better the classification effect of the component on the known category.
$$\sigma^{j} = - \sum\limits_{i} {\left| {x_{i} } \right.}^{j} - \left. {x_{i,nh}^{j} } \right| + \sum\limits_{i} {p_{i} \left| {x_{i} } \right.}^{j} - \left. {x_{i,nm}^{j} } \right|$$
(6)
Among them, \(p_{i}\) is the proportion of type l samples in all heterogeneous samples. After obtaining the importance values of all the components, the several feature components with the highest scores are selected as the new data feature set for training the classifier.
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(2)
Linear discriminant classification algorithm
For a group of training sets, try to project the examples in the training set on a certain line, so that the projections of the examples of the same category on this line are concentrated as much as possible, and the projection points of different categories are distributed as far as possible. According to this idea, suppose the mapping function f(x) is a linear discriminant function, namely:
$$f(x) = w^{T} x = w_{0}$$
(7)
x is a feature whose vector dimension is d, and w is a weight vector. The role of w is to map the high-dimensional vector x into the space, and the threshold weight is used to divide different categories.
$$\mu_{c} = \frac{1}{{N_{c} }}\sum\limits_{{x_{j} \in x_{c} }} {x_{j} }$$
(8)
$$X_{c} = \{ x_{j} |y_{j} = c\}$$
(9)
$$\sum {_{c} } = \sum\limits_{{x_{j} \in x_{c} }} {(x_{j} - \mu_{c} )} (x_{j} - \mu_{c} )^{T}$$
(10)
The idea of LDA classification method is to make the projections of similar examples as concentrated as possible and to enlarge the distance between classes as much as possible. According to this idea, the objective function J is defined as
$$J = \frac{{w^{T} S_{b} W}}{{w^{T} S_{W} W}}$$
(11)
Among them, \(S_{b}\) is the inter-class dispersion matrix and \(S_{w}\) is the intra-class dispersion matrix. Both are defined as follows:
$$S_{b} = \sum\limits_{i = 1}^{n} {m_{i} } (x_{i} - \mu )(x_{i} - \mu )^{T}$$
(12)
$$S_{w} = \sum\limits_{i = 1}^{n} {(x_{i} - \mu )(x_{i} - \mu )^{T} }$$
(13)
In the formula, m is the proportion of types of data in the overall data set S. Find the optimal solution of ω by introducing the operator:
$$S_{b} w = \lambda S_{w} w$$
(14)
For multi-class problems, the LDA algorithm provides the feature optimal projection surface to map the features of the data to a one-dimensional space and make judgments and decisions on the data category according to the decision rules. After the N-dimensional acceleration feature selected by the Relief-F algorithm, the posterior probability of being classified into category CY is
$$P(C_{Y} |f) = \frac{{P(f|C_{Y} )P(C_{y} )}}{P(f)}$$
(15)
