Paddle Stroke Analysis for Kayakers Using Wearable Technologies

Development of a video camera-type kayak motion capture system to measure water kayaking

Background

In kayaking, trunk motion is one of the important factors that prevent injury and improve performance. Kinematic studies in kayaking have been reported in laboratory settings using paddling simulators and ergometers. However, such studies do not reflect kayaking on water, the actual competitive environment. Therefore, we developed a video camera-type kayak motion capture system (KMCS) wherein action cameras were fixed to a kayak to capture images of markers attached to an athlete's body. This study aimed to compare the kinematic data between KMCS and an optical motion capture system (OMCS) in kayaking and to determine the accuracy of the KMCS analysis.

Methods

In a competition, five elite junior female kayak athletes performed kayak paddling under the unloaded condition using a kayak. The kayak was secured using a tri-folding bench and a towel, and twenty strokes were recorded during maximal paddling. One stroke was defined as the period from right catch to left catch, and the first six strokes were used to evaluate the accuracy. Trunk angles (tilting, turning, and rotation) were examined with the simultaneous use of KMCS and OMCS, and the differences between these systems were evaluated. To ensure reliability, intraclass correlation coefficient (ICC; a two-way mixed model for absolute agreement) was calculated for each angle. Furthermore, Bland-Altman analysis was performed to understand the agreement between the two systems.

Results

Root mean square errors (RMSEs) were 1.42° and 3.94° for turning and rotation, respectively, and mean absolute errors (MAEs) were 1.08° and 3.00° for turning and rotation, respectively. The RMSE and MAE for tilting were 2.43° and 1.76°, respectively, which indicated that the validity was comparable to that of other angles. However, the range of motion in tilting was lower than that in turning and rotation. Bland-Altman analysis showed good agreement in the total range of motion, with mean bias values of -0.84°, -0.07°, and -0.41° for tilting, turning, and rotation, respectively. The ICCs for tilting, turning, and rotation were 0.966, 0.985, and 0.973, respectively, and showed excellent reliability.

Conclusions

The newly developed KMCS effectively measured the trunk motion with good accuracy in kayaking. In future studies, we intend to use KMCS to measure kayaking on water and collect data for performance improvement and injury prevention.

An Ergonomic Assessment of Different Postures and Children Risk during Evacuations

Crawling is recommended for avoiding high heat and toxic fumes and for obtaining more breathable air during evacuations. Few studies have evaluated the effects of crawling on physical joints and velocity, especially in children. Based on motion capture technology, this study proposes a novel method of using wearable sensors to collect exposure (e.g., mean duration, frequency) on children’s joints to objectively quantify the impacts of different locomotion methods on physical characteristics. An on-site experiment was conducted in a kindergarten with 28 children (13 boys and 15 girls) of different ages (4–6 years old) who traveled up to 22 m in three different postures: upright walking (UW), stoop walking (SW), and knee and hand crawling (KHC). The results showed that: (1) The level of joint fatigue for KHC was heavier than bipedal walking (p < 0.05), which was evidenced by higher mean duration and frequency. There was no significant difference between UW and SW (p > 0.05). (2) The physical characteristics of the children in the different postures observed in this study were different (p < 0.05). The ankle was more fatigued than other joints during bipedal walking. Unlike infants, the wrists and hips of the children became fatigued while crawling. The key actions flexion/extension are more likely to induce joint fatigue vs. other actions. (3) Crawling velocity was significantly slower than the bipedal velocities, and UW was 10.6% faster than SW (p < 0.05). The bipedal walking velocity started to decrease after the children had travelled up to 13 m, while the KHC velocity started to decrease after traveling up to 11.6 m. (4) In a severe fire, the adoption of SW is suggested, as the evacuees can both evacuate quickly and avoid overworking their joints. (5) There were no significant differences in the age (p > 0.05) and gender (p > 0.05) of the children on the joints in any of the three postures. To conclude, KHC causes more damage to body joints compared to bipedal walking, as evidenced by higher exposure (mean duration, frequency), whereas UW and SW are similar in terms of the level of joint fatigue. The above findings are expected to provide a useful reference for future applications in the children’s risk assessment and in the prevention design of buildings.

Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar

Recent studies confirm the applicability of Inertial Measurement Unit (IMU)-based systems for human motion analysis. Notwithstanding, high-end IMU-based commercial solutions are yet too expensive and complex to democratize their use among a wide range of potential users. Less featured entry-level commercial solutions are being introduced in the market, trying to fill this gap, but still present some limitations that need to be overcome. At the same time, there is a growing number of scientific papers using not commercial, but custom do-it-yourself IMU-based systems in medical and sports applications. Even though these solutions can help to popularize the use of this technology, they have more limited features and the description on how to design and build them from scratch is yet too scarce in the literature. The aim of this work is two-fold: (1) Proving the feasibility of building an affordable custom solution aimed at simultaneous multiple body parts orientation tracking; while providing a detailed bottom-up description of the required hardware, tools, and mathematical operations to estimate and represent 3D movement in real-time. (2) Showing how the introduction of a custom 2.4 GHz communication protocol including a channel hopping strategy can address some of the current communication limitations of entry-level commercial solutions. The proposed system can be used for wireless real-time human body parts orientation tracking with up to 10 custom sensors, at least at 50 Hz. In addition, it provides a more reliable motion data acquisition in Bluetooth and Wi-Fi crowded environments, where the use of entry-level commercial solutions might be unfeasible. This system can be used as a groundwork for developing affordable human motion analysis solutions that do not require an accurate kinematic analysis.

Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model

Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients' gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.