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1. Utilizing Rhythmic Haptic Cueing in Arm Swing Training to Improve Gait Speed Among Older Adults

   https://doi.org/10.1007/s10439-024-03669-9  

   Khiyara, Ines; Sidaway, Ben; Hejrati, Babak (April 2025, Annals of Biomedical Engineering)

   NA (Ed.)

   Purpose: Current gait rehabilitation protocols for older adults typically attempt to effect changes in leg movements, while the role of arm movements is often ignored despite evidence of the neurological coupling of the upper and lower extremities. In the present work, we examine the effectiveness of a novel wearable haptic cueing system that targets arm swing to improve various gait parameters in older adults. Methods: Twenty participants ( M = 73.4 ± 6.2 years) were recruited to analyze their gait during normal and fast walking without haptic cueing. Vibrotactors attached to the arms were then used to give haptic cues that were designed to either increase or decrease arm swing cycle time. The effects of such cueing on gait symmetry and spatiotemporal parameters were then analyzed. Results: The presentation of the haptic cues significantly altered arm swing cycle time, increasing gait speed by 18.2% when arm cycle time was decreased, and a 12.3% decrease in gait speed when arm cycle time was lengthened. The response to haptic cues was immediate, emphasizing the tight coupling of the arm and legs in the production of gait. Spatiotemporal analysis revealed improvements in gait parameters and symmetry metrics, indicating enhanced coordination between limbs when using tactile cues. Subjective evaluations further supported the system’s potential for gait training. Conclusion: The results reveal the significant potential of the haptic cueing system to modulate gait through arm swing manipulation, leveraging interlimb neural coupling. This aligns with the growing need for home-based gait training solutions, particularly for the older population, and presents a novel approach that could be integrated into current gait rehabilitation practices. 

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2. Prediction of fall risk among community-dwelling older adults using a wearable system

   https://doi.org/10.1038/s41598-021-00458-5  

   Lockhart, Thurmon E.; Soangra, Rahul; Yoon, Hyunsoo; Wu, Teresa; Frames, Christopher W.; Weaver, Raven; Roberto, Karen A. (December 2021, Scientific Reports)

   Abstract Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor's potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals’ healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls. 

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3. 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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4. Wearable Sensor-Based Step Length Estimation During Overground Locomotion Using a Deep Convolutional Neural Network

   https://doi.org/10.1109/EMBC46164.2021.9630060  

   Jin, Heejoo; Kang, Inseung; Choi, Gayeon; Molinaro, Dean D.; Young, Aaron J. (November 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   Step length is a critical gait parameter that allows a quantitative assessment of gait asymmetry. Gait asymmetry can lead to many potential health threats such as joint degeneration, difficult balance control, and gait inefficiency. Therefore, accurate step length estimation is essential to understand gait asymmetry and provide appropriate clinical interventions or gait training programs. The conventional method for step length measurement relies on using foot-mounted inertial measurement units (IMUs). However, this may not be suitable for real-world applications due to sensor signal drift and the potential obtrusiveness of using distal sensors. To overcome this challenge, we propose a deep convolutional neural network-based step length estimation using only proximal wearable sensors (hip goniometer, trunk IMU, and thigh IMU) capable of generalizing to various walking speeds. To evaluate this approach, we utilized treadmill data collected from sixteen able-bodied subjects at different walking speeds. We tested our optimized model on the overground walking data. Our CNN model estimated the step length with an average mean absolute error of 2.89 ± 0.89 cm across all subjects and walking speeds. Since wearable sensors and CNN models are easily deployable in real-time, our study findings can provide personalized real-time step length monitoring in wearable assistive devices and gait training programs. 

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5. Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable

   https://doi.org/10.1017/wtc.2023.3  

   Davarzani, Samaneh; Saucier, David; Talegaonkar, Purva; Parker, Erin; Turner, Alana; Middleton, Carver; Carroll, Will; Ball, John E.; Gurbuz, Ali; Chander, Harish; et al (January 2023, Wearable Technologies)

   Abstract The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R -squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system. 

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