## 1 Introduction
The software source code was released for only 13 of these studies, and for 22 their data were made publicly available in support of reproducible research. These were typically small datasets that were used in support of the development and evaluation of a specific technological tool, or to validate a newly proposed method under specific conditions. As digital cameras and motion sensors became ubiquitous and are commonly used in mobile phones and office environments, various MSD detection methods have been developed based on commercially available devices [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Along with this, various resources have been created, including datasets made with cameras [13; 14; 15; 16; 17; 18; 19], wearable motion capture sensors [20; 21; 22; 23], Kinect devices [17; 24; 25; 26; 27], and many others. The advancement of automated technology, which is based on machine learning methods, heavily depends on the availability of resources (such as large datasets of video recordings, images, distance, pressure, or other sensor readings) needed for the training and fine-tuning of models. Furthermore, the development of models for specific conditions of interest depends on the availability of annotated datasets. This requires that some portions of the available data must be annotated by highly qualified professionals with extensive domain knowledge about the specific condition of interest, which is a tedious and costly process. Thus, resource sharing is quite important for fostering research and technology development in the area of MSD prevention, which remains a problem with significant social importance. Our primary motivation for developing the reported multimodal multisensory dataset in support of MSD prevention, MMD-MSD, was to provide a resource that would enable research of a wider scope and a multifaceted study of neck pain and back pain issues frequently developed by computer users. Specifically, the particular design of MMD-MSD opens new opportunities for research and technology development activities focused on modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal and valence), and performance (success rate on the Stroop test) of people working with a computer. Thus, MMD-MSD was conceived as a multimodal multisensory dataset that offers the unique opportunity to simultaneously study the physiological and psychological aspects of human-computer interaction in the most general setup for computer users. In addition to the long-term effects of pain and MSDs, we intended to investigate whether proper sitting posture would have any short-term effects on performance. This includes proper and typical standing and sitting positions (with and without a sitting pillow), the performance (success rate) on simple tasks requiring close attention (for instance, the Stroop test), and sitting behaviors while working with a computer within a typical office setup. The rest of this article is organized as follows: In the Section 2, we outline the dataset collection setup, protocol, data types, and tagging, along with the dataset structure. In the Section 3, we report baseline detection results that demonstrate two use cases of the MMD-MSD dataset. Finally, we conclude this article with a summary and conclusions. Appendix A outlines the MMD-MSD data organization and file formats.