Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs).

Optimizations in logistics require recognition and analysis of human activities. The potential of sensor-based human activity recognition (HAR) in logistics is not yet well explored. Despite a significant increase in HAR datasets in the past twenty years, no available dataset depicts activities in logistics. This contribution presents the first freely accessible logistics-dataset. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recreated. Fourteen subjects were recorded individually when performing warehousing activities using Optical marker-based Motion Capture (OMoCap), inertial measurement units (IMUs), and an RGB camera. A total of 758 min of recordings were labeled by 12 annotators in 474 person-h. All the given data have been labeled and categorized into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The dataset is deployed for solving HAR using deep networks.

This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner’s perspective.

The proposed concept of a Cellular Transport System shows the possibilities to increase the flexibility and changeability of facility logistics systems and enhances the ease of use of complex decentralized control systems. This contribution shows how to enhance these issues compared to conventional facility logistics systems, e.g. static conveyors, by using an autonomous vehicle swarm. Cellular Transport Systems are based on dedicated (cellular) material handling entities. Generally, these cells consist of autonomous transport vehicles (ATVs) or autonomous conveying modules. Various functions such as advanced sensor/actuator interoperation, highly reliable communication, localization and energy management are implemented in each of this cells, facilitating different forms of adaptive, anticipatory and collective behavior. Furthermore, Swarm Intelligence enables the creation of a collective that interacts and cooperates amongst each other in order to solve complex tasks.

This paper presents prospects for a human-centered design of future industrial labor in a framework depicting the dilemma between what is technologically feasible and labor-politically desirable, under the constraint of an economically reasonable design of work and technology. The analytical approach is the "socio-technical system" which assumes that new technologies will also result in personnel and organizational changes. These considerations are transformed into a framework which brings together the perspectives of human, technology and organization and ultimately leads to a complementary and ethical design of industrial labor under the conditions of a progressive digitization of manufacturing.