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1. Biomechanical Comparison of Human Walking Locomotion on Solid Ground and Sand

   https://doi.org/10.1115/1.4067842  

   Zhu, Chunchu; Chen, Xunjie; Yi, Jingang (April 2025, Journal of Biomechanical Engineering)

   Abstract Current studies on human locomotion focus mainly on solid ground walking conditions. In this paper, we present a biomechanical comparison of human walking locomotion on solid ground and sand. A novel dataset containing three-dimensional motion and biomechanical data from 20 able-bodied adults for walking locomotion on solid ground and sand is collected. We present the data collection methods and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics. The results reveal significant gait adaptations to the yielding terrain (i.e., sand), such as increased stance duration, reduced push-off force, and altered joint angles and moments. Specifically, the knee angle during the gait cycle on sand shows a delayed peak flexion and an increased overall magnitude, highlighting an adaptation to maintain stability on yielding terrain. These adjustments, including changes in joint timing and energy conservation mechanisms, provide insights into the motion control strategies humans adopt to navigate on yielding terrains. The dataset, containing synchronized ground reaction forces (GRFs) and kinematic data, offers a valuable resource for further exploration in foot–terrain interactions and human walking assistive devices development on yielding terrains. 

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2. Gait monitoring for older adults during guided walking: An integrated assistive robot and wearable sensor approach

   https://doi.org/10.1017/wtc.2022.23  

   Zhao, Qingya; Chen, Zhuo; Landis, Corey D.; Lytle, Ashley; Rao, Ashwini K.; Zanotto, Damiano; Guo, Yi (January 2022, Wearable Technologies)

   Abstract An active lifestyle can mitigate physical decline and cognitive impairment in older adults. Regular walking exercises for older individuals result in enhanced balance and reduced risk of falling. In this article, we present a study on gait monitoring for older adults during walking using an integrated system encompassing an assistive robot and wearable sensors. The system fuses data from the robot onboard Red Green Blue plus Depth (RGB-D) sensor with inertial and pressure sensors embedded in shoe insoles, and estimates spatiotemporal gait parameters and dynamic margin of stability in real-time. Data collected with 24 participants at a community center reveal associations between gait parameters, physical performance (evaluated with the Short Physical Performance Battery), and cognitive ability (measured with the Montreal Cognitive Assessment). The results validate the feasibility of using such a portable system in out-of-the-lab conditions and will be helpful for designing future technology-enhanced exercise interventions to improve balance, mobility, and strength and potentially reduce falls in older adults. 

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3. Low-force human–human hand interactions induce gait changes through sensorimotor engagement instead of direct mechanical effects

   https://doi.org/10.1038/s41598-024-53991-4  

   Wu, Mengnan; Hackney, Madeleine E.; Ting, Lena H. (February 2024, Scientific Reports)

   Abstract Physical human–robot interactions (pHRI) often provide mechanical force and power to aid walking without requiring voluntary effort from the human. Alternatively, principles of physical human–human interactions (pHHI) can inspire pHRI that aids walking by engaging human sensorimotor processes. We hypothesize that low-force pHHI can intuitively induce a person to alter their walking through haptic communication. In our experiment, an expert partner dancer influenced novice participants to alter step frequency solely through hand interactions. Without prior instruction, training, or knowledge of the expert’s goal, novices decreased step frequency 29% and increased step frequency 18% based on low forces (< 20 N) at the hand. Power transfer at the hands was 3–700 × smaller than what is necessary to propel locomotion, suggesting that hand interactions did not mechanically constrain the novice’s gait. Instead, the sign/direction of hand forces and power may communicate information about how to alter walking. Finally, the expert modulated her arm effective dynamics to match that of each novice, suggesting a bidirectional haptic communication strategy for pHRI that adapts to the human. Our results provide a framework for developing pHRI at the hand that may be applicable to assistive technology and physical rehabilitation, human-robot manufacturing, physical education, and recreation. 

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4. Superposition principle applies to human walking with two simultaneous interventions

   https://doi.org/10.1038/s41598-021-86840-9  

   Rasouli, Fatemeh; Kim, Seok Hun; Reed, Kyle B. (December 2021, Scientific Reports)

   null (Ed.)

   Abstract Gait rehabilitation therapies provide adjusted sensory inputs to modify and retrain walking patterns in an impaired gait. Asymmetric walking is a common gait abnormality, especially among stroke survivors. Physical therapy interventions using adaptation techniques (such as treadmill training, auditory stimulation, visual biofeedback, etc.) train gait toward symmetry. However, a single rehabilitation therapy comes up short of affecting all aspects of gait performance. Multiple-rehabilitation therapy applies simultaneous stimuli to affect a wider range of gait parameters and create flexible training regiments. Understanding gait responses to individual and jointly applied stimuli is important for developing improved and efficient therapies. In this study, 16 healthy subjects participated in a four-session experiment to study gait kinetics and spatiotemporal outcomes under training. Each session consisted of two stimuli, treadmill training and auditory stimulation, with symmetric or asymmetric ratios between legs. The study hypothesizes a linear model for gait response patterns. We found that the superposition principle largely applies to the gait response under two simultaneous stimuli. The linear models developed in this study fit the actual data from experiments with the r-squared values of 0.95 or more. 

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5. Machine Learning Based Adaptive Gait Phase Estimation Using Inertial Measurement Sensors

   https://doi.org/10.1115/DMD2019-3266  

   Yang, Jianfu; Huang, Tzu-Hao; Yu, Shuangyue; Yang, Xiaolong; Su, Hao; Spungen, Ann M.; Tsai, Chung-Ying (April 2019, 2019 Design of Medical Devices Conference)

   This paper presents a portable inertial measurement unit (IMU)-based motion sensing system and proposed an adaptive gait phase detection approach for non-steady state walking and multiple activities (walking, running, stair ascent, stair descent, squat) monitoring. The algorithm aims to overcome the limitation of existing gait detection methods that are time-domain thresholding based for steady-state motion and are not versatile to detect gait during different activities or different gait patterns of the same activity. The portable sensing suit is composed of three IMU sensors (wearable sensors for gait phase detection) and two footswitches (ground truth measurement and not needed for gait detection of the proposed algorithm). The acceleration, angular velocity, Euler angle, resultant acceleration, and resultant angular velocity from three IMUs are used as the input training data and the data of two footswitches used as the training label data (single support, double support, swing phase). Three methods 1) Logistic Regression (LR), 2) Random Forest Classifier (RF), and 3) Artificial Neural Network (NN) are used to build the gait phase detection models. The result shows our proposed gait phase detection with Random Forest Classifier can achieve 98.94% accuracy in walking, 98.45% in running, 99.15% in stair-ascent, 99.00% in stair-descent, and 99.63% in squatting. It demonstrates that our sensing suit can not only detect the gait status in any transient state but also generalize to multiple activities. Therefore, it can be implemented in real-time monitoring of human gait and control of assistive devices. 

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