{"id":110808,"date":"2025-09-24T14:35:03","date_gmt":"2025-09-24T12:35:03","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=110808"},"modified":"2025-09-29T15:07:28","modified_gmt":"2025-09-29T13:07:28","slug":"mtm-analysis-motion-capture-data","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/mtm-analysis-motion-capture-data\/","title":{"rendered":"Derivation of MTM Analyses from Motion Capture Data"},"content":{"rendered":"\n

Between 2007 and 2023, average annual labor productivity per working hour rose by only 0.7% [1]. An increase in labor productivity can therefore represent a significant competitive advantage, particularly for companies with a high proportion of manual activities. Labor productivity compares the output in produced parts with the input in paid working hours. As the output is often determined by the market, analyses tend to focus on the input [2].<\/p>\n\n\n\n

Depending on the type of production, MTM analyses (MTM stands for Methods-Time Measurement) are frequently used. For example, work processes are divided into basic motions for mass production (MTM-1\u00ae) or basic operations for batch production (MTM-UAS\u00ae) in order to determine basic times [3]. Creating these analyses can be time-consuming and requires expert knowledge.<\/p>\n\n\n\n

However, they provide the basis for planning and improving work processes. In practice, according to the MTM ASSOCIATION e. V. (MTMA), an MTM expert needs around five hours to fully analyze a one-minute motion sequence using the MTM-1\u00ae method [4]. Due to the limited expert knowledge in companies and the high effort involved, such analyses are only carried out to a limited extent. This means that considerable productivity potential remains untapped.<\/p>\n\n\n\n

To address this problem, MTMA has developed the MTMmotion\u00ae tool. MTMmotion\u00ae generates MTM analyses in various MTM systems (MTM-1\u00ae, MTM-UAS\u00ae) simultaneously from the input data of digital technologies and additional metadata such as product features [5]. In [6], it was shown how a motion capture system (MoCap system) can be used with a digital assistance system to generate input data for MTMmotion\u00ae.<\/p>\n\n\n\n

While the MoCap system captures motion, the digital assistance system is used to record additional metadata, perform various analyses using the motion data and visualize the analysis results. The following article validates the generated MTM analyses. For this purpose, the method developed for a MoCap system and MTMmotion\u00ae (MoCap method) is compared with a manual MTM-1\u00ae analysis in terms of effort, the accuracy of the results and applicability in different planning phases.<\/p>\n\n\n\n

The MTM-1\u00ae method<\/strong><\/p>\n\n\n\n

The MTM-1\u00ae method, developed in the United States during the 1940s, represents a system of predetermined times and has since served as a foundational analytical tool for the analysis of manual work processes. It facilitates the systematic description and structuring of human work with the objective of enabling efficient work planning and process optimization. At the core of the MTM methodology are standardized process building blocks, which enable the description, quantification, and design of work sequences.<\/p>\n\n\n\n

Motion capture systems and digital assistance systems<\/h2>\n\n\n\n

MoCap technologies record and document motion and transfer this data to a digital human model [9]. There are various methods for motion capture, including optical, electromechanical, electromagnetic and acoustic systems [10]. An electromechanical MoCap system from Xsens is used in this article. The tracking suit contains 17 sensors with inertial and magnetic measuring units [11].<\/p>\n\n\n\n

Digital assistance systems support people in their activities by helping them to recognize and evaluate information [12]. The Institute of Production Management and Technology\u2019s web-based software platform supports the use of digital assistance systems on various end devices such as smartphones and tablets, regardless of the platform.<\/p>\n\n\n\n

The Xsens MoCap system was linked to the digital assistance system using the manufacturer\u2019s software development kit. It records position and orientation data and transmits it wirelessly to the digital assistance system so that various productivity and ergonomics analyses can be carried out. The assistance system also allows data to be entered manually, for example to record metadata (such as parts lists). Furthermore, the results of the MTM analysis can be retrieved and visualized via the assistance system.<\/p>\n\n\n\n

MTMmotion\u00ae<\/h2>\n\n\n\n

The aim of the development of MTMmotion\u00ae was to enable all technologies that generate or process human motion data to derive valid MTM analyses so that users can analyze and design workplaces [13, 14]. To fulfill this goal, MTMmotion\u00ae uses a translation algorithm that can translate motion data transmitted via an interface into MTM analyses. Essential motion data includes motion lengths and directions as well as the specific MTM-influencing variables [13].<\/p>\n\n\n\n

The translation algorithm checks the information for correctness and adds missing data. If a technology fails to capture all necessary information, default values are added. The information is then translated into MTM process modules and arranged in chronological order [13, 14].<\/p>\n\n\n\n

The MTMmotion\u00ae interface consists of an object list and six channels that map motion sequences. To describe a work process, it is necessary to record arm motions and link them to the objects. In the following, the arm motions are used to explain how the data is generated using the MoCap method (see [6] for a detailed description). Any motion performed with the hands or fingers to pick up an object, move it in space or use it for a specific operation is defined as an arm motion.<\/p>\n\n\n\n

Arm motions are only recorded if the handling of an object serves a specific purpose, for example bringing an object to its intended place of use or putting a tool down. Various influencing variables must be specified, such as the object to be handled (objectId from the object list), the start and end time of the motions and specific influencing variables (for example motion distance, grasp type, supply, force) in order to describe the workflow more precisely. If this information is missing, default values are used [5].<\/p>\n\n\n\n

For the MoCap method, this data is determined as follows:<\/p>\n\n\n\n

    \n
  1. Detailing the work processes:<\/strong> The user must define the sequence of objects (material and equipment) and assembly operations in the work processes.<\/li>\n<\/ol>\n\n\n\n
      \n
    1. Processing the motion data:<\/strong> The MoCap system calculates the hand and body motions with the start and end times and motion lengths and automatically assigns them to the objects from the work process. If errors occur, the user must correct the assignment.<\/li>\n<\/ol>\n\n\n\n

      These two steps generate the interface data for MTMmotion\u00ae to create various MTM analyses (see section MTMmotion\u00ae). The creation of the interface data is described in more detail in [6].<\/p>\n\n\n\n

      Comparing the MoCap method and manual method<\/h2>\n\n\n\n

      In this section, the method developed for a MoCap system and MTMmotion\u00ae is compared with a manual MTM-1\u00ae analysis in terms of effort, the accuracy of the results and applicability in different planning phases. For this purpose, a practical example was used in which the right pedal unit of a bicycle is assembled. The assembly process comprises four operations in which a total of four components are assembled: Chainring, crank arm, chainring bolts and a pedal. Two allen wrenches of different sizes are used as tools. The total duration of the work sequence is 58 seconds.<\/p>\n\n\n\n

      To determine the effort involved, the steps for preparing and performing the MTM analysis were recorded using the MoCap method and the manual method. The results are shown in Figure 1<\/strong>.<\/p>\n\n\n\n

      \"Figure
      Figure 1: Steps for preparing and performing the MTM-1 analysis.<\/em><\/figcaption><\/figure>\n\n\n\n

      MoCap method: <\/strong>Nine steps are required to prepare and carry out the MTM analysis, with the measurement of the components and equipment, the detailing of the work processes and the preparation of the transaction data taking up the largest proportion of the time. However, the time required for detailing the operations is only very high for the first run and is reduced for subsequent runs, as the elements from the previous run can be adopted and adapted. Step 6 was carried out twice because the actual process deviated from the target process in the first run.<\/p>\n\n\n\n

      Manual method:<\/strong> The preparation and execution of the analysis comprises a total of five steps, with the preparation of the MTM analysis taking the most time. It should be noted that the test was carried out under laboratory conditions, which, according to the MTM experts, reduces the time required.<\/p>\n\n\n\n

      In the example, the time required to prepare and perform a manual MTM-1\u00ae analysis is around 2.5 times longer than with the MoCap method. This factor would increase with a longer workflow, as the preparation of the manual MTM analysis is significantly more time-consuming than the steps carried out using the MoCap method.<\/p>\n\n\n\n

      The correct use of MTMmotion\u00ae with the MoCap method for arm motions is validated below. For this purpose, input data for MTMmotion\u00ae was provided by the MoCap method on the one hand and by MTM experts on the other.<\/p>\n\n\n\n

      MTMmotion\u00ae with the MoCap method:<\/strong> All data (for example length, width, height) for the objects to be used is recorded manually. In addition, the motion lengths for the arm motions are transferred from the MoCap system if this input is required. Default values are used manually for all other influencing variables. Due to the sometimes very complex motion sequences, assumptions are made in some cases, depending on the specific properties of the object and the motion sequence, in order to generate the channel inputs automatically.<\/p>\n\n\n\n

      Deviations may occur due to the use of default values and other simplified assumptions of the MoCap method. To be able to analyze the application of MTMmotion\u00ae in the example, a complete data set was created by MTM experts (MTM complete).<\/p>\n\n\n\n

      MTM complete<\/strong>: All channel inputs and specific influencing factors, such as the grasp type or symmetry, were recorded manually by MTM experts. <\/p>\n\n\n\n

      A manual MTM-1\u00ae analysis was also created for the comparison. The entire validation procedure is shown in Figure 2<\/strong>.<\/p>\n\n\n\n

      \"Figure
      Figure 2: Validation procedure.<\/em><\/figcaption><\/figure>\n\n\n\n

      Comparison of the input data (\u0394input):<\/strong> The MoCap method was able to capture 74% of the required input data. The missing 26% can be attributed to the following two main causes:<\/p>\n\n\n\n

        \n
      1. Specific motions such as turning the finished part before placing it cannot be recorded with the MoCap method.<\/li>\n<\/ol>\n\n\n\n
          \n
        1. The channel C26 (HoldObject) was not implemented.<\/li>\n<\/ol>\n\n\n\n

          In the second step, the correctly identified channel data was compared regarding the deviations in the distances and times as well as the specific influencing variables:<\/p>\n\n\n\n