Estimation of Tibiofemoral and Patellofemoral Joint Forces during Squatting and Kneeling

This study examined the differences of knee joint forces between lowering to, or rising from squat, and typical final postures of squatting and kneeling. A biomechanical model of the lower limb was configured considering large knee flexion angles, multiple floor contact points, and the soft tissue contact between the thigh and calf. Inverse dynamics were used to determine muscle and compressive joint forces in the tibiofemoral and patellofemoral joints. Data were obtained from a group of 13 male subjects by means of 3D motion capturing, two force plates, a pressure-sensitive pad, and electromyography. During lowering into the kneeling/squatting positions and rising from them, the model exhibited the anticipated high maximum forces of 2.6 ± 0.39 body weight (BW) and 3.4 ± 0.56 BW in the tibiofemoral and patellofemoral joints. Upon attainment of the static terminal squatting and kneeling positions, the forces fell considerably, remaining within a range of between 0.5 and 0.7 BW for the tibiofemoral joint and 0.9 to 1.1 BW for the patellofemoral joint. The differences of the knee joint forces between the final postures of squatting and kneeling remained on average below 0.25 BW and were significant only for the tibiofemoral joint force.

Context-Aware Human Activity Recognition in Industrial Processes

The automatic, sensor-based assessment of human activities is highly relevant for production and logistics, to optimise the economics and ergonomics of these processes. One challenge for accurate activity recognition in these domains is the context-dependence of activities: Similar movements can correspond to different activities, depending on, e.g., the object handled or the location of the subject. In this paper, we propose to explicitly make use of such context information in an activity recognition model. Our first contribution is a publicly available, semantically annotated motion capturing dataset of subjects performing order picking and packaging activities, where context information is recorded explicitly. The second contribution is an activity recognition model that integrates movement data and context information. We empirically show that by using context information, activity recognition performance increases substantially. Additionally, we analyse which of the pieces of context information is most relevant for activity recognition. The insights provided by this paper can help others to design appropriate sensor set-ups in real warehouses for time management.

A Passive, Skin-Attachable Multi-Sensing Patch Based on Semi-Liquid Alloy Ni-GaIn for Wireless Epidermal Signal Monitoring and Body Motion Capturing

Wearable integrated systems that rely on liquid metal commonly require an extremely complicated, high-cost fabrication process, while lacking multiple sensing functions without conductive wires connected to external electronic systems. A multi-sensing wearable patch independent from sophisticated manufacturing method and excessive use of wires has yet to be developed. Herein, we introduce a wireless, battery-free, and skin-attachable patch with multiple sensing capacities, utilizing a low-budget, less time-consuming and design-customizable fabrication method. In an effort to achieve our goal, the general sensing system architecture is promoted, which consists of a semi-liquid alloy Ni-GaIn based strain sensor and a co-designed near-field-communication (NFC) tag integrating thermistor, photoresistor, as well as sensor interface circuits, enabling energy-autonomous power supply and wireless data transmission. In human volunteers, the patch was mounted on the skin surface to demonstrate real-time temperature and light intensity signal monitoring. Further evaluation of body motion capturing involved finger bending and swallowing, demonstrating the feasibility of practical applications in different scenarios. Continuous and simultaneous multi-type signal sensing using the wearable patch should enrich the dimensions of measurements of body response to daily activities, unveiling the potential for remote human health monitoring, advanced human–machine interfaces, and other applications of interest.

Optimization and evaluation of motion sequences of an averaged human motion model by using kinematic techniques and new evaluation methods

Signal processing, pattern recognition as well as modelling and simulation require a large amount of reference data, both for the development of new methods and for their evaluation. Depending on the application, the availability of databases is rather low. In the field of biosignal processing with a focus on the functionalization of furniture for nursing and hospital facilities, a database from a motion capturing system (MoCap), and a method to generate averaged human motion sequences was presented in subsequent works by our research group. Evaluations revealed that the averaged motion sequences partly contain artifacts caused by the averaging and thus are not directly usable. To use the averaged motion sequences e.g., in simulation tasks, this paper presents an extension with kinematic methods to combine averaged motion sequences and to suppress and thus optimize inappropriate motion artifacts by error correction. To check whether the results are usable after the processing steps, four evaluation criteria are proposed. The evaluation of the resulting motion sequences shows that sequences are generated which do not fully correspond to human motion sequences but are well suited for simulation tasks.

2Vita-B Physical: An Intelligent Home Rehabilitation System Based on Microsoft Azure Kinect

Active rehabilitation is an exercise-based program designed to improve the level of function of people with motor disabilities. The effectiveness of such programs is strongly influenced by the correctness of the exercise execution. An exercise done incorrectly could even lead to a worsening of the health status. For this reason, specialists are required to guide the patient during the execution of an exercise. The drastic reduction of the costs of motion tracking systems has paved the way to the implementation of virtual assistant software able to automatically assess the correctness of an exercise. In this paper 2Vita-B Physical is presented, a rehabilitation software system properly designed to support both 1) the patients, by guiding them in the correct execution of an exercise; and 2) the physiotherapists, by allowing them to remotely check the progress of a patient. The motion capturing in 2Vita-B is performed by using the recently released Microsoft Kinect Azure DK. Thus, the system is easy to use and completely non-invasive. Besides the hardware and software requirements of the system, the results of a preliminary usability evaluation of the system conducted with 29 users is also reported. The results achieved are promising and provide evidence of the high usability of 2Vita-B Physical as home rehabilitation system.

Kinästhetische Interferenzen

In einer künstlerisch basierten Studie zwischen architektonischer Entwurfspraxis und Tanzwissenschaft untersucht Dominik Mohs die Wechselwirkungen von Raumgestaltung und kinästhetischer Wahrnehmung. Rudolph von Labans choreographisches Denken überführt er dafür in eine experimentelle Versuchsanordnung, in der er Raumbildungsprozesse und Antriebe von Bewegungsimprovisationen mit Methoden der Tanzwissenschaften und Motion Capturing analysiert. Tänzerische Bewegung, verstanden als Einfühlung in den Raum und Ausdrucksgeschehen leiblich zentrierter Wahrnehmung, wird damit zur Grundlage, um den von August Schmarsow eingeführten architektonischen Raumbegriff zu hinterfragen.

fastSW: Efficient Piecewise Linear Approximation of Quaternion-Based Orientation Sensor Signals for Motion Capturing with Wearable IMUs

In the past decade, inertial measurement sensors have found their way into many wearable devices where they are used in a broad range of applications, including fitness tracking, step counting, navigation, activity recognition, or motion capturing. One of their key features that is widely used in motion capturing applications is their capability of estimating the orientation of the device and, thus, the orientation of the limb it is attached to. However, tracking a human’s motion at reasonable sampling rates comes with the drawback that a substantial amount of data needs to be transmitted between devices or to an end point where all device data is fused into the overall body pose. The communication typically happens wirelessly, which severely drains battery capacity and limits the use time. In this paper, we introduce fastSW, a novel piecewise linear approximation technique that efficiently reduces the amount of data required to be transmitted between devices. It takes advantage of the fact that, during motion, not all limbs are being moved at the same time or at the same speed, and only those devices need to transmit data that actually are being moved or that exceed a certain approximation error threshold. Our technique is efficient in computation time and memory utilization on embedded platforms, with a maximum of 210 instructions on an ARM Cortex-M4 microcontroller. Furthermore, in contrast to similar techniques, our algorithm does not affect the device orientation estimates to deviate from a unit quaternion. In our experiments on a publicly available dataset, our technique is able to compress the data to 10% of its original size, while achieving an average angular deviation of approximately 2° and a maximum angular deviation below 9°.

Structure-from-Motion, Capturing Geological Samples v1

Structure-from-Motion (SfM) is a photogrammetry process that creates 3D models from overlapping 2D images. This protocol focuses on its application related to geological and geophysical samples. The samples includes fossil, hand samples and rocks. This is a recommended practice to be used later for the publication on United States Geological Survey website.