CN110704293A - Three-dimensional multi-channel interface availability assessment method - Google Patents
Three-dimensional multi-channel interface availability assessment method Download PDFInfo
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Abstract
The invention relates to a three-dimensional multi-channel interface availability evaluation method, and belongs to the field of human-computer interaction. The evaluation indexes are determined by extracting multi-source data generated in the interaction process of the three-dimensional multi-channel user interface and the single-channel user interface, the evaluation indexes of the three-dimensional multi-channel user interface are linearly fitted through the evaluation result of the availability indexes of the single-channel interface, and the weight of each evaluation index in the overall availability evaluation result is calculated through an analytic hierarchy process, so that the interface availability evaluation aiming at the three-dimensional multi-channel interaction is realized. The invention has the advantages that: the interactive efficiency, the error rate, the expressive force and the user satisfaction degree are selected as the evaluation indexes of the three-dimensional multi-channel user interface, the performance of the multi-channel user interface can be better reflected, the multi-channel is evaluated by using a single-channel evaluation result, a basis is provided for the selection of the channel, and a final usability evaluation result is obtained.
Description
Technical Field
The invention belongs to the field of human-computer interaction, and particularly relates to a three-dimensional multi-channel interface usability assessment method.
Background
The human-computer interface is an important component of a human-computer interaction system and plays a role in transmitting and exchanging information between human and computers. Modern computer users are already familiar with input devices including mice, touch screens, etc., output devices including displays, tablets, cell phones, etc., interactive modes such as drag-and-drop and zoom, however, these interface components are generally not suitable for today's non-traditional computing environments and applications, where many interfaces operate in real or virtual three-dimensional spaces, which we refer to as three-dimensional user interfaces. The purpose of enhancing the interface usability can be achieved by adopting a multi-channel interaction mode of voice, gestures, touch and the like for a three-dimensional interface, in addition, the multi-channel interaction mode provides support for data visualization on hardware equipment such as mobile phones, AR/VR and large-scale displays and the like, and the three-dimensional multi-channel user interface has a wide application field. The usability evaluation of the user interface plays an important role in interface development and interface product testing, and the process of the usability evaluation of the user interface refers to the process of systematically collecting usability data of the interactive interface, evaluating the usability data and improving the usability data. The goal of usability is to indicate that the user interface is effective, easy to learn, efficient, easy to remember, error-free, and meets the user's satisfaction requirements. The international organization for standardization assesses the usability of user interfaces in terms of effectiveness, interaction efficiency, and user satisfaction. The three-dimensional multi-channel user interface design often comprises the requirements of enhancing the interactive reality, being suitable for users with multiple scenes and different professional backgrounds, improving the interactive efficiency and the like and meeting the new interactive concept, so that new requirements are provided for usability evaluation indexes.
In the current technical background, there are many methods for evaluating the usability of a user interface, which are typically a sequential evaluation method, a test bed evaluation method, a component evaluation method, etc., but there is no accepted method for evaluating the usability of a three-dimensional multi-channel user interface. Since the three-dimensional multi-channel user interface needs to process the interactive information from different cognitive resources of the user, the interactive channels are also diversified, and the selection of the interactive channels by different users is also different. Therefore, it is difficult to find a universal evaluation method for a multi-channel user interface. Compared with a single-channel interactive interface, the multi-channel interface is the most important of the expansion of an interactive mode, namely, the interactive task is realized through different input modes of multiple channels. Therefore, the availability of a single channel has a direct influence on the availability of multiple channels, and the method for judging the availability of the multiple channels through the availability index of the single channel improves the efficiency of availability evaluation of a multi-channel user interface and provides a basis for effectively expanding the single-channel interface based on the availability.
Currently, a unified and efficient usability assessment method is not provided for a three-dimensional multi-channel user interface. Chinese patent "a system interface availability quantitative evaluation method" (publication No. 104503916a) proposes a system interface availability quantitative evaluation method, which mainly solves the problem of absolute evaluation results for different tasks during availability evaluation by comparing with an ideal scheme, and does not provide a systematic evaluation method for complex interaction. The chinese patent "a three-dimensional pen type interactive interface usability assessment method" (publication No. 107391289a) proposes a usability assessment method for a three-dimensional pen type interactive interface by analyzing three-dimensional pen type interactive behaviors, but the method is directed to pen type interaction, and cannot be used for solving the usability assessment problem of a multi-channel user interface including complex interactive modes such as voice, gesture, and the like. Chinese patent "a method for detecting usability of man-machine interface of complex system" (publication No. 103713728A) proposes an interface evaluation method based on eye movement signal, however, because eye movement signal only extracts the unicity of eyeball observation screen information, it is not suitable for three-dimensional pen type interaction with high user operation complexity. The chinese patent "a smartphone human-machine interface availability evaluation method based on a user model" (publication No. 105701015a) provides an availability evaluation method for a smartphone interactive interface according to an eye movement signal, does not give an evaluation result qualitatively and quantitatively, and only points out specific problems.
Disclosure of Invention
The invention provides a three-dimensional multi-channel interface availability evaluation method, which comprises the steps of extracting multi-source data generated in the interaction process of a three-dimensional multi-channel user interface and a single-channel user interface, determining evaluation indexes of the multi-source data, linearly fitting the evaluation indexes of the availability indexes of the single-channel interface to obtain the evaluation indexes of the three-dimensional multi-channel user interface, and calculating the weight of each evaluation index in the overall availability evaluation result by an analytic hierarchy process to realize interface availability evaluation aiming at three-dimensional multi-channel interaction.
The technical scheme adopted by the invention is as follows: comprises the following steps:
the method comprises the following steps: determining an availability evaluation index of a three-dimensional multi-channel interface according to the three-dimensional multi-channel interaction function;
step two: selecting an interactive task for evaluation, completing the interactive task in a single-channel interactive mode and a multi-channel interactive mode, and extracting multi-source data for index evaluation in the two interactive modes;
step three: performing index conversion on the extracted multi-source data;
step four: for each evaluation index, carrying out linear regression analysis on a user evaluation result in a single-channel mode and an evaluation result in a multi-channel mode to obtain a weight of each channel of each evaluation index;
step five: and constructing a weight model for multi-source data under multi-channel interaction by using an analytic hierarchy process, determining the weight occupied by each evaluation index in availability evaluation, and obtaining a three-dimensional multi-channel interface availability evaluation result.
The three-dimensional multi-channel user interface in the first step of the invention adopts three input channels of voice, gesture and electronic pen for interaction in a three-dimensional space, and the usability evaluation indexes of the three-dimensional multi-channel user interface are determined as interaction efficiency, error rate, expressive force and user satisfaction.
The extraction of multi-source data in two interaction modes in the step two of the invention comprises the following steps:
in single channel interaction mode:
(1) the following data need to be collected for the evaluation index of interaction efficiency: time t for completing interactive task one to three through voice channel11，t12，t13(ii) a Time t for completing interaction task one to three by gesture channel21，t22，t23B, carrying out the following steps of; time t for completing interactive task one to three by electronic pen channel31，t32，t33；
(2) The following data need to be collected for this evaluation index of error rate: number m of false identifications for completing interactive tasks of one to three through voice channel11，m12，m13(ii) a Wrong recognition times m for gesture channel to finish interaction tasks of one to three21，m22，m23(ii) a Error recognition times m for completing interactive tasks by electronic pen channel31，m32，m33；
(3) For the expressive force index, after the user completes the task, the user research tool is adopted to obtain the evaluation result of the experimenter on the index, and the voice channel completes the expressive force scoring result r of the interaction task from one to three11，r12，r13(ii) a Gesture channel finishes expressive force scoring result r of interaction tasks from one to three21，r22，r23(ii) a Expressive force scoring result r for completing interaction tasks one to three through electronic pen channel31，r32，r33；
(4) For the user satisfaction index, after the user finishes the task, the evaluation result of the experimenter on the index is obtained by adopting a user research tool, and the satisfaction scoring result s of the voice channel finishing the interactive task from one to three is obtained11，s12，s13(ii) a Satisfaction scoring result s for gesture channel to finish interaction tasks from one to three21，s22，s23(ii) a Satisfaction scoring result s for completing interactive tasks by electronic pen channel31，s32，s33；
The same task is accomplished in a multi-pass mode:
(1) the following data need to be collected for the evaluation index of interaction efficiency: the time for completing the interaction task from one to three is t41，t42，t43；
(2) The following data need to be collected for this evaluation index of error rate: the number of times of error identification for completing the interactive tasks from one to three is m41，m42，m43；
(3) After the user completes the task, the expressiveness of the three channels is scored respectively by using a user research tool, AttrakDiff: r is41，r42，r43，
(4) After the user completes the task, the satisfaction of the interaction of the three channels is scored by using a user research tool AttrakDiff: s41，s42，s43。
The user research tool of the invention adopts AttrakDiff.
The index conversion of the interactive task completion time in the third step of the invention comprises the following steps:
for the index of the interaction efficiency, the time for completing the interaction tasks is measured, the normalization processing is carried out on the time for completing the interaction tasks for facilitating the subsequent linear regression processing and usability evaluation, the time for completing the interaction tasks under three single channels and multiple channels of the voice, the gesture and the electronic pen is changed into decimal between 0 and 1 by the following formula, and the efficiency eta of the three interaction tasks of the voice channel is obtained11，η12，η13Respectively as follows:
efficiency eta of three interaction tasks of gesture channel21，η22，η23Respectively as follows:
efficiency eta of three interactive tasks of electronic pen channel31，η32，η33Respectively as follows:
efficiency eta of multi-channel three interactive tasks41，η42，η43Respectively as follows:
eta for obtaining interaction efficiency matrix of voice channel1Representing, gesture channel interaction efficiency matrix by η2Representing by η, the interaction efficiency matrix of the electronic pen channel3Expressing, by η, the interaction efficiency matrix under multiple channels4Is shown, then1To eta4Respectively as follows:
for the index of error rate, in the interactive experiment process, each interactive task is tested in each interactive mode, the error times are recorded, the percentage of the error times in repeated experiments is calculated, and the error rate mu of the voice channel for completing the interactive tasks from one to three is calculated11，μ12，μ13(ii) a Error rate mu of gesture channel for completing interaction tasks from one to three21，μ22，μ23(ii) a Error rate mu of one to three for completing interactive tasks by electronic pen channel31，μ32，μ33Error rate of multi-channel interactive task one to three, mu41，μ42，μ43And obtaining the decimal number between 0 and 1 of the error rate value to obtain the error rate matrix mu under the voice channel1Error rate matrix μ under gesture channel2Error rate matrix mu under electronic pen channel3Error rate matrix μ under multiple channels4Then μ1To mu4Respectively as follows:
for the two indexes of expressive force and user satisfaction, the evaluation results of experimenters on the two indexes are obtained by adopting a user research tool:
expressive force r for completing interaction tasks one to three through voice channel11，r12，r13；
Expressive force r for completing interaction tasks from one to three through gesture channel21，r22，r23；
Expressive force r for completing interaction tasks by electronic pen channel31，r32，r33；
Expressive force r of multi-channel interaction tasks from one to three41，r42，r43；
Satisfaction degree scoring result s of user completing interaction tasks from one to three on voice channel11，s12，s13(ii) a Satisfaction degree scoring result s of one to three tasks of completing interaction task for gesture channel21，s22，s23(ii) a Satisfaction scoring result s of one to three interactive tasks completed by electronic pen channel31，s32，s33(ii) a Satisfaction degree scoring result s of completing interactive task from one to three for multi-channel41，s42，s43；
Because the user research tool divides the evaluation result into seven grades, and 7 grades are quantized into seven decimal numbers between 0 and 1 after the evaluation is finished, the burden of the user is reduced for the evaluation mode respectively, and the user can directly make evaluation to obtain the expression force matrix r under the voice channel1Expression force matrix r under gesture channel2Expression force matrix r under electronic pen channel3Expression force matrix r under multiple channels4Then r is1、r2、r3、r4Respectively as follows:
obtaining use under voice channelFamily satisfaction degree matrix s1User satisfaction matrix s under gesture channel2User satisfaction matrix s under electronic pen channel3User satisfaction matrix s under multiple channels4Then s1、s2、s3、s4Respectively as follows:
the user research tool described in the present invention employs AttrakDiff.
The step four of the linear regression analysis of the user evaluation result in the single channel mode and the evaluation result in the multi-channel mode comprises the following steps:
carrying out linear regression analysis on the user evaluation result in the single-channel mode and the evaluation result in the multi-channel mode, respectively taking four evaluation indexes of interaction efficiency, error rate, expression and user satisfaction in the multi-channel interaction mode as response variables of linear regression, taking the corresponding index of a single channel as a prediction variable, and for any one index of the four indexes of interaction efficiency, error rate, expression and user satisfaction, expressing the response variable by A, namely the index value in the multi-channel mode, and expressing the index value by A1，A2，A3Respectively representing prediction variables, namely index values under a single channel, and establishing a multiple linear regression model shown by the following formula:
A＝k1A1+k2A2+k3A3+e
wherein k is1、k2、k3The weights of three single channels of voice, gesture and electronic pen are respectively, e is an error, and when A is multi-channel interaction, the value of the observation matrix A worth of efficiency is divided into four conditions: (η)41，η42，η43)T、(μ41，μ42，μ43)T、(r41，r42，r43)TAnd(s)41，s42，s43)T；，A1，A2，A3Respectively is a voice,An observation value matrix when three single channels of the gesture and the electronic pen interact:
wherein A is1There are four cases of η1，μ1，r1，s1When A is1Get eta1When a is11＝η11，a21＝η21，a31＝η31(ii) a When A is1Taking mu1When a is21＝μ11，a21＝μ21，a31＝μ31(ii) a When A is1Get r1When a is11＝r11，a21＝r21，a31＝r31(ii) a When A is1Get s1When a is not present11＝s11，a21＝s21，a31＝s31；
A2There are four cases of2，μ2，r2，s2When A is2Get eta2When a is12＝η12，a22＝η22，a32＝η32(ii) a When A is2Get, mu2When a12＝μ12，a22＝μ22，a32＝μ32(ii) a When A is2Get r2When a is12＝r12，a22＝r22，a32＝r32(ii) a When A is1Get s2When a is12＝s12，a22＝s22，a32＝s32；
A3There are four cases of3，μ3，r3，s3When A is3Get eta3When a is13＝η13，a23＝η23，a33＝η33(ii) a When A is3Taking mu3When a is13＝μ13，a23＝μ23，a33＝μ33(ii) a When A is3Get r3When a is13＝r13，a23＝r23，a33＝r33；A3Get s3When a is13＝s13，a23＝s23，a33＝s33；
With A 'representing the observation matrix, A' can be represented as:
the weight value of each single-channel index is the parameter matrix k ═ k (k) of the regression model1，k2，k3)TThe least squares method is used to estimate the following formula:
B＝(A'TA')-1A'A＝(b1，b2，b3)
wherein, b1，b2，b3And the weights of the single-channel efficiency values are respectively the weights when the evaluation indexes of three channels, namely voice, gesture and electronic pen, evaluate the index values of the three-dimensional multi-channel interface.
The method for determining the weight occupied by each evaluation index in the fifth step of the invention comprises the following steps:
the method for calculating the weight of the four evaluation indexes of the interaction efficiency, the error rate, the expressive force and the user satisfaction in the usability evaluation by using the analytic hierarchy process comprises the following steps:
(1) establishing a hierarchical analysis model of availability
The method comprises the specific division method that the target layer is a three-dimensional multi-channel interface, the usability is recorded as M, the factors influencing the target M can be divided into four evaluation indexes, namely the criterion layer comprises interaction efficiency, error rate, expressive force and user satisfaction, and M ═ eta [ { eta [ ] [, where M is4，μ4，r4，s4Dividing the influence factors of each criterion of the criterion layer by using each index of a single channel as a sub-criterion layer to obtain a criterion sub-layer eta4＝{η1，η2，η3}，μ4＝{μ1，μ2，μ3}，r4＝{r1，r2，r3}，s4＝{s1，s2，s3The method comprises the following steps that (1) any of three channels can be selected to be combined during channel selection, so that seven schemes can be calculated;
(2) constructing a judgment matrix and solving a weight vector:
for the rule sublayer, the interaction efficiency, the error rate, the expression and the user satisfaction of three single channels of voice, gesture and electronic pen are influenced by the corresponding indexes in multiple channels, and the corresponding weight is represented by a formula B ═ A'TA')-1A'A＝(b1，b2，b3) Is found in which b1，b2，b3The weight value of the factor is the standard sublayer influence;
constructing a criterion layer judgment matrix, comparing the evaluation indexes with each other by relative importance degrees, and according to the following table,
relative importance value table
| Importance level | Of absolute importance | Is very important | Is very important | Of little importance | Of equal importance |
| Value taking | 9 | 7 | 5 | 3 | 1 |
The relative importance degree is divided into five grades, the values are 1 to 9, and a judgment matrix P is obtained4x4，
Wherein p isij(i represents the ith row and j represents the jth column) is the relative importance degree value of the index corresponding to the row and the column of the element, the specific value is shown in table 1, when i is equal to j, p isijThe values are as follows: -1, when i ≠ j, pijThe value is taken according to a relative importance value taking table, and the maximum characteristic value phi of the matrix P is calculatedmaxAnd a feature vector, i.e. a weight vector (q) of the four indicators for availability1，q2，q3，q4)，q1，q2，q3And q is4The weights of the interaction efficiency, the error rate, the expressive force and the user satisfaction degree in the multi-channel process account for the usability respectively.
The invention has the advantages that:
(1) the interactive efficiency, the error rate, the expressive force and the user satisfaction degree are selected as evaluation indexes of the three-dimensional multi-channel user interface, and the performance of the multi-channel user interface can be better reflected.
(2) The weight of each index single-channel evaluation result in the multi-channel evaluation result is obtained by performing linear regression analysis on the user evaluation result in the single-channel mode and the evaluation result in the multi-channel mode, and the multi-channel evaluation can be realized by using the single-channel evaluation result.
(3) The method for evaluating the multi-channel user interface by using the single-channel evaluation result is convenient for evaluating the interface with different interaction channels expanded on the basis of the existing interface and provides a basis for selecting the channel.
(4) For the evaluation of the total availability, the invention adopts an analytic hierarchy process to establish a weight model based on four indexes of interaction efficiency, error rate, expressive force and user satisfaction, and obtains the final availability evaluation result.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of the AttrakDiff user evaluation of the present invention;
FIG. 3 is an experimental interface for accomplishing an interaction task;
FIG. 4 is a flow chart of an analytic hierarchy process;
FIG. 5 is an influence factor hierarchy chart.
Detailed Description
Comprises the following steps:
the method comprises the following steps: determining an availability evaluation index of a three-dimensional multi-channel interface according to the three-dimensional multi-channel interaction function;
the three-dimensional multi-channel user interface adopts three input channels of voice, gesture and electronic pen for interaction in a three-dimensional space, and usability evaluation indexes of the three-dimensional multi-channel user interface are determined as interaction efficiency, error rate, expressive force and user satisfaction;
step two: selecting an interactive task for evaluation, completing the interactive task in a single-channel interactive mode and a multi-channel interactive mode, and extracting multi-source data for index evaluation in the two interactive modes;
the platform of the interaction task experiment evaluated by the user is based on the system of Chinese patent 'a three-dimensional large-space multi-channel pen type interaction system' (patent number ZL 201611157044.0); the selected first to third interactive tasks are respectively to enable an object in a scene to rotate by a specific angle, select a specific position and zoom the object, and specifically realize the scene of the interactive task as shown in fig. 3, aiming at the interactive task one, firstly enabling a user to interactively rotate the earth in the scene to a specific angle through three single channels of voice, gesture and electronic pen, and then enabling the user to self-select one or more interactive channels to complete the rotation task at the same angle; aiming at the second interactive task, firstly, a user selects a specified country in the earth through three single-channel interactions of voice, gesture and electronic pen respectively, and then the user selects the country by selecting one or more interactive channels; aiming at the third interactive task, firstly, a user zooms the earth in a scene through voice, gestures and an electronic pen respectively, then the user can select one or more interactive channels to finish the same zooming, in the operation, in order to ensure the same zooming proportion, a method of marking on an interactive screen and selecting a fixed point as a zooming center is adopted, before an experiment, unified training is carried out on experimenters, the capability of selecting the interactive channels independently is reached, in the experiment process, the time for finishing the interactive task, the times of errors and other multi-source data are recorded, and after the experiment, the user evaluates the interactive experience;
in single channel interaction mode:
(1) the following data need to be collected for the evaluation index of interaction efficiency: time t for completing interactive task one to three through voice channel11，t12，t13(ii) a Time t for completing interaction task one to three by gesture channel21，t22，t23B, carrying out the following steps of; time t for completing interactive task one to three by electronic pen channel31，t32，t33；
(2) The following data need to be collected for this evaluation index of error rate: number m of false identifications for completing interactive tasks of one to three through voice channel11，m12，m13(ii) a Wrong recognition times m for gesture channel to finish interaction tasks of one to three21，m22，m23(ii) a Error recognition times m for completing interactive tasks by electronic pen channel31，m32，m33；
(3) For the expressive force index, after the user completes the task, the user research tool Attrak Diff is adopted to obtain the evaluation result of the experimenter on the index, and the voice channel completes the expressive force scoring result r of the interaction tasks from one to three11，r12，r13(ii) a Gesture channel finishes expressive force scoring result r of interaction tasks from one to three21，r22，r23(ii) a Expressive force scoring result r for completing interaction tasks one to three through electronic pen channel31，r32，r33；
(4) For the user satisfaction index, after the user completes the task, the user research tool Attrak Diff is adopted to obtain the evaluation result of the experimenter on the index, and the voice channel completes the satisfaction scoring result s of one to three interactive tasks11，s12，s13(ii) a Satisfaction scoring result s for gesture channel to finish interaction tasks from one to three21，s22，s23(ii) a Satisfaction scoring result s for completing interactive tasks by electronic pen channel31，s32，s33；
The same task is accomplished in a multi-pass mode:
(1) the following data need to be collected for the evaluation index of interaction efficiency: the time for completing the interaction task from one to three is t41，t42，t43；
(2) The following data need to be collected for this evaluation index of error rate: the number of times of error identification for completing the interactive tasks from one to three is m41，m42，m43；
(3) After the user completes the task, the expressiveness of the three channels is scored respectively by using a user research tool, AttrakDiff: r is41，r42，r43，
(4) After the user completes the task, the satisfaction of the interaction of the three channels is scored by using a user research tool AttrakDiff: s41，s42，s43。
Step three: performing index conversion on the extracted multi-source data;
for the index of the interaction efficiency, the time for completing the interaction tasks is measured, the normalization processing is carried out on the time for completing the interaction tasks for facilitating the subsequent linear regression processing and usability evaluation, the time for completing the interaction tasks under three single channels and multiple channels of the voice, the gesture and the electronic pen is changed into decimal between 0 and 1 by the following formula, and the efficiency eta of the three interaction tasks of the voice channel is obtained11，η12，η13Respectively as follows:
efficiency eta of three interaction tasks of gesture channel21，η22，η23Respectively as follows:
efficiency eta of three interactive tasks of electronic pen channel31，η32，η33Respectively as follows:
efficiency eta of multi-channel three interactive tasks41，η42，η43Respectively as follows:
eta for obtaining interaction efficiency matrix of voice channel1Representing, gesture channel interaction efficiency matrix by η2Representing by η, the interaction efficiency matrix of the electronic pen channel3Expressing, by η, the interaction efficiency matrix under multiple channels4Is shown, then1To eta4Respectively as follows:
for the index of error rate, in the process of carrying out interactive experiment, each interactive task carries out 8 times of experiment under each interactive mode, records the error times, calculates the percentage of the error times in 8 repeated experiments, and finishes the error rate mu of interactive task from one to three by the voice channel11，μ12，μ13(ii) a Error rate mu of gesture channel for completing interaction tasks from one to three21，μ22，μ23(ii) a Error rate mu of one to three for completing interactive tasks by electronic pen channel31，μ32，μ33Error rate of multi-channel interactive task one to three, mu41，μ42，μ43And obtaining the decimal number between 0 and 1 of the error rate value to obtain the error rate matrix mu under the voice channel1Error rate matrix μ under gesture channel2Error rate matrix mu under electronic pen channel3Error rate matrix μ under multiple channels4Then μ1To mu4Respectively as follows:
for the two indexes of expressive force and user satisfaction, the user research tool AttrakDiff shown in FIG. 2 is used to obtain the evaluation results of experimenters on the two indexes,
expressive force r for completing interaction tasks one to three through voice channel11，r12，r13；
Expressive force r for completing interaction tasks from one to three through gesture channel21，r22，r23；
Expressive force r for completing interaction tasks by electronic pen channel31，r32，r33；
Expressive force r of multi-channel interaction tasks from one to three41，r42，r43；
Satisfaction degree scoring result s of user completing interaction tasks from one to three on voice channel11，s12，s13(ii) a Satisfaction degree scoring result s of one to three tasks of completing interaction task for gesture channel21，s22，s23(ii) a Satisfaction scoring result s of one to three interactive tasks completed by electronic pen channel31，s32，s33(ii) a Satisfaction degree scoring result s of completing interactive task from one to three for multi-channel41，s42，s43；
Attrak Diff will evaluate due to the user study toolThe result is divided into seven levels, as shown in fig. 2, the seven circles from left to right respectively represent the seven levels of the satisfaction degree, the user does not need to score during evaluation, only needs to select the corresponding circle, and after the evaluation is finished, the 7 levels are quantized into seven decimal numbers between 0 and 1, so that the burden of the user is reduced for the evaluation mode respectively, and the user can make evaluation more directly to obtain the expressive force matrix r under the voice channel1Expression force matrix r under gesture channel2Expression force matrix r under electronic pen channel3Expression force matrix r under multiple channels4Then r is1、r2、r3、r4Respectively as follows:
obtaining a user satisfaction matrix s under a voice channel1User satisfaction matrix s under gesture channel2User satisfaction matrix s under electronic pen channel3User satisfaction matrix s under multiple channels4Then s1、s2、s3、s4Respectively as follows:
step four: for each evaluation index, carrying out linear regression analysis on a user evaluation result in a single-channel mode and an evaluation result in a multi-channel mode to obtain the weight of the three channels of voice, gesture and electronic pen for each evaluation index;
carrying out linear regression analysis on the user evaluation result in the single-channel mode and the evaluation result in the multi-channel mode, respectively taking four evaluation indexes of interaction efficiency, error rate, expression and user satisfaction in the multi-channel interaction mode as response variables of linear regression, taking the corresponding index of a single channel as a prediction variable, and for any one index of the four indexes of interaction efficiency, error rate, expression and user satisfaction, expressing the response variable by A, namely the index value in the multi-channel mode, and expressing the index value by A1，A2，A3Respectively representing prediction variables, namely index values under a single channel, and establishing a multiple linear regression model shown by the following formula:
A＝k1A1+k2A2+k3A3+e
wherein k is1、k2、k3The weights of three single channels of voice, gesture and electronic pen are respectively, e is an error, and when A is multi-channel interaction, the value of the observation matrix A worth of efficiency is divided into four conditions: (η)41，η42，η43)T、(μ41，μ42，μ43)T、(r41，r42，r43)TAnd(s)41，s42，s43)T；，A1，A2，A3Are observed value matrixes of three single channels of voice, gesture and electronic pen,
wherein A is1There are four cases of η1，μ1，r1，s1When A is1Get eta1When a is11＝η11，a21＝η21，a31＝η31(ii) a When A is1Taking mu1When a is21＝μ11，a21＝μ21，a31＝μ31(ii) a When A is1Get r1When a is11＝r11，a21＝r21，a31＝r31(ii) a When A is1Get s1When a is not present11＝s11，a21＝s21，a31＝s31；
A2There are four cases of2，μ2，r2，s2When A is2Get eta2When a is12＝η12，a22＝η22，a32＝η32(ii) a When A is2Get, mu2When a12＝μ12，a22＝μ22，a32＝μ32(ii) a When A is2Get r2When a is12＝r12，a22＝r22，a32＝r32(ii) a When A is1Get s2When a is12＝s12，a22＝s22，a32＝s32；
A3There are four cases of3，μ3，r3，s3When A is3Get eta3When a is13＝η13，a23＝η23，a33＝η33(ii) a When A is3Taking mu3When a is13＝μ13，a23＝μ23，a33＝μ33(ii) a When A is3Get r3When a is13＝r13，a23＝r23，a33＝r33；A3Get s3When a is13＝s13，a23＝s23，a33＝s33；
With A 'representing the observation matrix, A' can be represented as:
the weight value of each single-channel index is the parameter matrix k ═ k (k) of the regression model1，k2，k3)TThe least squares method is used to estimate the following formula:
B＝(A'TA')-1A'A＝(b1，b2，b3)
wherein, b1，b2，b3When the index values of the three-dimensional multi-channel interface are evaluated by using evaluation indexes of three channels of voice, gesture and electronic pen, the weight occupied by each single-channel efficiency value is taken;
step five: constructing a weight model for multi-source data under multi-channel interaction by using an analytic hierarchy process, and determining the weight occupied by each evaluation index in availability evaluation; obtaining a three-dimensional multi-channel interface availability evaluation result;
the analytic hierarchy process is a quantitative and qualitative combined, hierarchical and systematic analytical method, the analytic hierarchy process is used for calculating the weight values of the four evaluation indexes of the interactive efficiency, the error rate, the expressive power and the user satisfaction in the usability evaluation, and a flow chart for calculating the weight values by adopting the analytic hierarchy process is shown in figure 4 and comprises the following steps:
(1) establishing a hierarchical analysis model of availability
The analytic hierarchy process is to decompose the elements related to decision into object, criterion, scheme and other layers, and then to do qualitative and quantitative analysis, when using chromatographic analysis process to evaluate the usability, when dividing the criterion layer, the related factors are decomposed into several layers from top to bottom according to different attributes, because the usability of the interface is influenced by many factors, if the usability influence factor of the three-dimensional multi-channel interface is layered directly, the complexity and the iteration of the layer model will be caused. Since the multiple channels are evaluated based on the evaluation index of the single channel, the hierarchical analysis model is relatively simple, and the specific division method is shown in fig. 5, if the target layer is a three-dimensional multi-channel interface, the usability of which is recorded as M, the factors affecting the target M can be divided into four evaluation indexes, that is, if the criterion layer includes interaction efficiency, error rate, expressiveness and user satisfaction, then M ═ η4，μ4，r4，s4Dividing the influence factors of each criterion of the criterion layer by using each index of a single channel as a sub-criterion layer to obtain a criterion sub-layer eta4＝{η1，η2，η3}，μ4＝{μ1，μ2，μ3}，r4＝{r1，r2，r3}，s4＝{s1，s2，s3And as any of the three channels can be selected for combination during channel selection, calculation can be performedThere are seven schemes altogether;
(2) constructing a judgment matrix and solving a weight vector:
for the rule sublayer, the interaction efficiency, the error rate, the expression and the user satisfaction of three single channels of voice, gesture and electronic pen are influenced by the corresponding indexes in multiple channels, and the corresponding weight is represented by a formula B ═ A'TA')-1A'A＝(b1，b2，b3) Is found in which b1，b2，b3The weight value of the factor is the standard sublayer influence;
constructing a criterion layer judgment matrix, comparing the evaluation indexes with each other by relative importance degrees, according to the table 1,
TABLE 1 relative importance value-taking Table
| Importance level | Of absolute importance | Is very important | Is very important | Of little importance | Of equal importance |
| Value taking | 9 | 7 | 5 | 3 | 1 |
The relative importance degree is divided into five grades, the values are 1 to 9, and a judgment matrix P is obtained4x4，
Wherein p isij(i represents the ith row and j represents the jth column) is the relative importance degree value of the index corresponding to the row and the column of the element, the specific value is shown in table 1, when i is equal to j, p isijThe values are as follows: -1, when i ≠ j, pijThe value is taken according to a relative importance value taking table, and the maximum characteristic value phi of the matrix P is calculatedmaxAnd a feature vector, i.e. a weight vector (q) of the four indicators for availability1，q2，q3，q4)，q1，q2，q3And q is4The weights of the interaction efficiency, the error rate, the expressive force and the user satisfaction degree in the multi-channel process account for the usability respectively.
The following describes the consistency check of the present invention.
In order to determine whether the importance result reflected by the weight vector is consistent with the logical importance ranking, consistency check is required. Introducing consistency index of CI ═ phimaxN)/(n-1), where ΦmaxIn order to determine the maximum eigenvalue of the matrix P, n is the number of indices, i.e. n is 4, an average random consistency index RI and a random consistency ratio CR is introduced, as shown in table 2,
TABLE 2 average random consistency index RI value-taking table in analytic hierarchy process
Since n is 4, the value of RI is 0.89 by looking up the table, and CR is calculated to be 0.025. The consistency test of the analytic hierarchy process is judged by comparing the size relationship between CR and 0.1, and when CR is less than 0.1, the obtained judgment matrix meets the consistency requirement; when CR is greater than 0.1, the obtained judgment matrix is not satisfied with the consistency requirement, the obtained weight vector can be judged to have acceptable consistency by calculation, and the consistency requirement is satisfied because 0.025< 0.1.
Claims (8)
1. A three-dimensional multi-channel interface availability assessment method is characterized by comprising the following steps:
the method comprises the following steps: determining an availability evaluation index of a three-dimensional multi-channel interface according to the three-dimensional multi-channel interaction function;
step two: selecting an interactive task for evaluation, completing the interactive task in a single-channel interactive mode and a multi-channel interactive mode, and extracting multi-source data for index evaluation in the two interactive modes;
step three: performing index conversion on the extracted multi-source data;
step four: for each evaluation index, carrying out linear regression analysis on a user evaluation result in a single-channel mode and an evaluation result in a multi-channel mode to obtain a weight of each channel of each evaluation index;
step five: and constructing a weight model for multi-source data under multi-channel interaction by using an analytic hierarchy process, determining the weight occupied by each evaluation index in availability evaluation, and obtaining a three-dimensional multi-channel interface availability evaluation result.
2. The three-dimensional multi-channel interface usability assessment method according to claim 1, characterized in that: and step one, the three-dimensional multi-channel user interface adopts three input channels of voice, gesture and electronic pen for interaction in a three-dimensional space, and usability evaluation indexes of the three-dimensional multi-channel user interface are determined as interaction efficiency, error rate, expressive force and user satisfaction.
3. The three-dimensional multi-channel interface usability assessment method according to claim 1, characterized in that: the second step of extracting the multi-source data under two interaction modes comprises the following steps:
in single channel interaction mode:
(1) the following data need to be collected for the evaluation index of interaction efficiency: time t for completing interactive task one to three through voice channel11，t12，t13(ii) a Time t for completing interaction task one to three by gesture channel21，t22，t23B, carrying out the following steps of; time t for completing interactive task one to three by electronic pen channel31，t32，t33；
(2) The following data need to be collected for this evaluation index of error rate: number m of false identifications for completing interactive tasks of one to three through voice channel11，m12，m13(ii) a Wrong recognition times m for gesture channel to finish interaction tasks of one to three21，m22，m23(ii) a Error recognition times m for completing interactive tasks by electronic pen channel31，m32，m33；
(3) For the expressive force index, after the user completes the task, the user research tool is adopted to obtain the evaluation result of the experimenter on the index, and the voice channel completes the expressive force scoring result r of the interaction task from one to three11，r12，r13(ii) a Gesture channel finishes expressive force scoring result r of interaction tasks from one to three21，r22，r23(ii) a Expressive force scoring result r for completing interaction tasks one to three through electronic pen channel31，r32，r33；
(4) For the user satisfaction index, after the user finishes the task, the evaluation result of the experimenter on the index is obtained by adopting a user research tool, and the satisfaction scoring result s of the voice channel finishing the interactive task from one to three is obtained11，s12，s13(ii) a Satisfaction scoring result s for gesture channel to finish interaction tasks from one to three21，s22，s23(ii) a Satisfaction scoring result s for completing interactive tasks by electronic pen channel31，s32，s33；
The same task is accomplished in a multi-pass mode:
(1) the following data need to be collected for the evaluation index of interaction efficiency: the time for completing the interaction task from one to three is t41，t42，t43；
(2) The following data need to be collected for this evaluation index of error rate: the number of times of error identification for completing the interactive tasks from one to three is m41，m42，m43；
(3) After the user completes the task, the expressiveness of the three channels is scored respectively by using a user research tool, AttrakDiff: r is41，r42，r43，
(4) After the user completes the task, the satisfaction of the interaction of the three channels is scored by using a user research tool AttrakDiff: s41，s42，s43。
4. The three-dimensional multi-channel interface usability assessment method according to claim 3, wherein the user research tool employs AttrakDiff.
5. The three-dimensional multi-channel interface usability assessment method according to claim 1, wherein the index conversion for the interaction task completion time in the step three includes:
for the index of the interaction efficiency, the time for completing the interaction tasks is measured, the normalization processing is carried out on the time for completing the interaction tasks for facilitating the subsequent linear regression processing and usability evaluation, the time for completing the interaction tasks under three single channels and multiple channels of the voice, the gesture and the electronic pen is changed into decimal between 0 and 1 by the following formula, and the efficiency eta of the three interaction tasks of the voice channel is obtained11，η12，η13Respectively as follows:
efficiency eta of three interaction tasks of gesture channel21，η22，η23Respectively as follows:
efficiency eta of three interactive tasks of electronic pen channel31，η32，η33Respectively as follows:
efficiency eta of multi-channel three interactive tasks41，η42，η43Respectively as follows:
eta for obtaining interaction efficiency matrix of voice channel1Representing, gesture channel interaction efficiency matrix by η2Representing by η, the interaction efficiency matrix of the electronic pen channel3Expressing, by η, the interaction efficiency matrix under multiple channels4Is shown, then1To eta4Respectively as follows:
for the index of error rate, in the interactive experiment process, each interactive task is tested in each interactive mode, the error times are recorded, the percentage of the error times in repeated experiments is calculated, and the error rate mu of the voice channel for completing the interactive tasks from one to three is calculated11，μ12，μ13(ii) a Error rate mu of gesture channel for completing interaction tasks from one to three21，μ22，μ23(ii) a Error rate mu of one to three for completing interactive tasks by electronic pen channel31，μ32，μ33Error rate of multi-channel interactive task one to three, mu41，μ42，μ43And obtaining the decimal number between 0 and 1 of the error rate value to obtain the error rate matrix mu under the voice channel1Error rate matrix μ under gesture channel2Error rate matrix mu under electronic pen channel3Error rate matrix μ under multiple channels4Then μ1To mu4Respectively as follows:
for the two indexes of expressive force and user satisfaction, the evaluation results of experimenters on the two indexes are obtained by adopting a user research tool:
expressive force r for completing interaction tasks one to three through voice channel11，r12，r13；
Expressive force r for completing interaction tasks from one to three through gesture channel21，r22，r23；
Expressive force r for completing interaction tasks by electronic pen channel31，r32，r33；
Expressive force r of multi-channel interaction tasks from one to three41，r42，r43；
Satisfaction degree scoring result s of user completing interaction tasks from one to three on voice channel11，s12，s13(ii) a Satisfaction degree scoring result s of one to three tasks of completing interaction task for gesture channel21，s22，s23(ii) a Satisfaction scoring result s of one to three interactive tasks completed by electronic pen channel31，s32，s33(ii) a Satisfaction degree scoring result s of completing interactive task from one to three for multi-channel41，s42，s43；
Because the user research tool divides the evaluation result into seven grades, and 7 grades are quantized into seven decimal numbers between 0 and 1 after the evaluation is finished, the burden of the user is reduced for the evaluation mode respectively, and the user can directly make evaluation to obtain the expression force matrix r under the voice channel1Expression force matrix r under gesture channel2Expression force matrix r under electronic pen channel3Expression moment array under multiple channelsr4Then r is1、r2、r3、r4Respectively as follows:
obtaining a user satisfaction matrix s under a voice channel1User satisfaction matrix s under gesture channel2User satisfaction matrix s under electronic pen channel3User satisfaction matrix s under multiple channels4Then s1、s2、s3、s4Respectively as follows:
6. the three-dimensional multi-channel interface usability assessment method according to claim 5, wherein the user research tool employs AttrakDiff.
7. The three-dimensional multi-channel interface usability assessment method according to claim 1, wherein the linear regression analysis of the user evaluation result in the single channel mode and the assessment result in the multi-channel mode in the step four is:
carrying out linear regression analysis on the user evaluation result in the single-channel mode and the evaluation result in the multi-channel mode, respectively taking four evaluation indexes of interaction efficiency, error rate, expression and user satisfaction in the multi-channel interaction mode as response variables of linear regression, taking the corresponding index of a single channel as a prediction variable, and for any one index of the four indexes of interaction efficiency, error rate, expression and user satisfaction, expressing the response variable by A, namely the index value in the multi-channel mode, and expressing the index value by A1，A2，A3Respectively representing prediction variables, namely index values under a single channel, and establishing a multiple linear regression model shown by the following formula:
A＝k1A1+k2A2+k3A3+e
wherein k is1、k2、k3The weights of three single channels of voice, gesture and electronic pen are respectively, e is an error, and when A is multi-channel interaction, the value of the observation matrix A worth of efficiency is divided into four conditions: (η)41，η42，η43)T、(μ41，μ42，μ43)T、(r41，r42，r43)TAnd(s)41，s42，s43)T；，A1，A2，A3The three single-channel interaction observation value matrixes of voice, gesture and electronic pen are respectively:
wherein A is1There are four cases of η1，μ1，r1，s1When A is1Get eta1When a is11＝η11，a21＝η21，a31＝η31(ii) a When A is1Taking mu1When a is21＝μ11，a21＝μ21，a31＝μ31(ii) a When A is1Get r1When a is11＝r11，a21＝r21，a31＝r31(ii) a When A is1Get s1When a is not present11＝s11，a21＝s21，a31＝s31；
A2There are four cases of2，μ2，r2，s2When A is2Get eta2When a is12＝η12，a22＝η22，a32＝η32(ii) a When A is2Get, mu2When a12＝μ12，a22＝μ22，a32＝μ32(ii) a When A is2Get r2When a is12＝r12，a22＝r22，a32＝r32(ii) a When A is1Get s2When a is12＝s12，a22＝s22，a32＝s32；
A3There are four cases of3，μ3，r3，s3When A is3Get eta3When a is13＝η13，a23＝η23，a33＝η33(ii) a When A is3Taking mu3When a is13＝μ13，a23＝μ23，a33＝μ33(ii) a When A is3Get r3When a is13＝r13，a23＝r23，a33＝r33；A3Get s3When a is13＝s13，a23＝s23，a33＝s33；
With A 'representing the observation matrix, A' can be represented as:
the weight value of each single-channel index is the parameter matrix k ═ k (k) of the regression model1，k2，k3)TThe least squares method is used to estimate the following formula:
B＝(A'TA')-1A'A＝(b1，b2，b3)
wherein, b1，b2，b3And the weights of the single-channel efficiency values are respectively the weights when the evaluation indexes of three channels, namely voice, gesture and electronic pen, evaluate the index values of the three-dimensional multi-channel interface.
8. The three-dimensional multi-channel interface availability evaluation method according to claim 1, wherein the weight value occupied by each evaluation index in the fifth step is determined by the following method:
the method for calculating the weight of the four evaluation indexes of the interaction efficiency, the error rate, the expressive force and the user satisfaction in the usability evaluation by using the analytic hierarchy process comprises the following steps:
(1) establishing a hierarchical analysis model of availability
The method comprises the specific division method that the target layer is a three-dimensional multi-channel interface, the usability is recorded as M, the factors influencing the target M can be divided into four evaluation indexes, namely the criterion layer comprises interaction efficiency, error rate, expressive force and user satisfaction, and M ═ eta [ { eta [ ] [, where M is4，μ4，r4，s4Dividing the influence factors of each criterion of the criterion layer by using each index of a single channel as a sub-criterion layer to obtain a criterion sub-layer eta4＝{η1，η2，η3}，μ4＝{μ1，μ2，μ3}，r4＝{r1，r2，r3}，s4＝{s1，s2，s3The method comprises the following steps that (1) any of three channels can be selected to be combined during channel selection, so that seven schemes can be calculated;
(2) constructing a judgment matrix and solving a weight vector:
for the rule sublayer, the interaction efficiency, the error rate, the expression and the user satisfaction of three single channels of voice, gesture and electronic pen are influenced by the corresponding indexes in multiple channels, and the corresponding weight is represented by a formula B ═ A'TA')-1A'A＝(b1，b2，b3) Is found in which b1，b2，b3The weight value of the factor is the standard sublayer influence;
constructing a criterion layer judgment matrix, comparing the evaluation indexes with each other by relative importance degrees, and according to the following table,
relative importance value table
The relative importance degree is divided into five grades, the values are 1 to 9, and a judgment matrix P is obtained4x4，
Wherein p isij(i represents the ith row and j represents the jth column) is the relative importance degree value of the index corresponding to the row and the column of the element, the specific value is shown in table 1, when i is equal to j, p isijThe values are as follows: -1, when i ≠ j, pijThe value is taken according to a relative importance value taking table, and the maximum characteristic value phi of the matrix P is calculatedmaxAnd a feature vector, i.e. a weight vector (q) of the four indicators for availability1，q2，q3，q4)，q1，q2，q3And q is4Interaction efficiency, error rate, expressiveness and user fullness in multiple channels, respectivelyThe ideality takes the weight of availability.
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