An official website of the United States government
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.
more »
« less
Assessment of Cognitive Fatigue from Gait Cycle Analysis
Cognitive Fatigue (CF) is the decline in cognitive abilities due to prolonged exposure to mentally demanding tasks. In this paper, we used gait cycle analysis, a biometric method related to human locomotion to identify cognitive fatigue in individuals. The proposed system in this paper takes two asynchronous videos of the gait of individuals to classify if they are cognitively fatigued or not. We leverage the pose estimation library OpenPose, to extract the body keypoints from the frames in the videos. To capture the spatial and temporal information of the gait cycle, a CNN-based model is used in the system to extract the embedded features which are then used to classify the cognitive fatigue level of individuals. To train and test the model, a gait dataset is built from 21 participants by collecting walking data before and after inducing cognitive fatigue using clinically used games. The proposed model can classify cognitive fatigue from the gait data of an individual with an accuracy of 81%.
more »
« less
- Award ID(s):
- 2226164
- PAR ID:
- 10463932
- Date Published:
- Journal Name:
- Technologies
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2227-7080
- Page Range / eLocation ID:
- 18
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Modeling individual-specific gait dynamics based on kinematic data could aid the development of gait rehabilitation robotics by enabling robots to predict the user’s gait kinematics with and without external inputs, such as mechanical or electrical perturbations. Here we address a current limitation of data-driven gait models, which do not yet predict human gait dynamics nor responses to perturbations. We used Switched Linear Dynamical Systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill during normal gait and during gait perturbed by functional electrical stimulation (FES) to the ankle muscles. Our SLDS models were able to generate joint angle trajectories in each of four gait phases, as well as across an entire gait cycle, given initial conditions and gait phase information. Because the SLDS dynamics matrices encoded significant coupling across joints that differed across individuals, we compared the SLDS predictions to that of a kinematic model, where the joint angles were independent. Joint angle trajectories generated by SLDS and kinematic models were similar over time horizons of a few milliseconds, but SLDS models provided better predictions of gait kinematics over time horizons of up to a second. We also demonstrated that SLDS models can infer and predict individual-specific responses to FES during swing phase. As such, SLDS models may be a promising approach for online estimation and control of and human gait dynamics, allowing robotic control strategies to be tailored to an individual’s specific gait coordination patterns.more » « less
-
In this paper, we propose a novel, generalizable, and scalable idea that eliminates the need for collecting Radio Frequency (RF) measurements, when training RF sensing systems for human-motion-related activities. Existing learning-based RF sensing systems require collecting massive RF training data, which depends heavily on the particular sensing setup/involved activities. Thus, new data needs to be collected when the setup/activities change, significantly limiting the practical deployment of RF sensing systems. On the other hand, recent years have seen a growing, massive number of online videos involving various human activities/motions. In this paper, we propose to translate such already-available online videos to instant simulated RF data for training any human-motion-based RF sensing system, in any given setup. To validate our proposed framework, we conduct a case study of gym activity classification, where CSI magnitude measurements of three WiFi links are used to classify a person's activity from 10 different physical exercises. We utilize YouTube gym activity videos and translate them to RF by simulating the WiFi signals that would have been measured if the person in the video was performing the activity near the transceivers. We then train a classifier on the simulated data, and extensively test it with real WiFi data of 10 subjects performing the activities in 3 areas. Our system achieves a classification accuracy of 86% on activity periods, each containing an average of 5.1 exercise repetitions, and 81% on individual repetitions of the exercises. This demonstrates that our approach can generate reliable RF training data from already-available videos, and can successfully train an RF sensing system without any real RF measurements. The proposed pipeline can also be used beyond training and for analysis and design of RF sensing systems, without the need for massive RF data collection.more » « less
-
This research presents PACE (Providing Authentication through Computational Gait Evaluation), a novel methodology for gait-based authentication leveraging the power of deep learning algorithms. The primary objective of PACE is to enhance the security and efficiency of user authentication mechanisms by capitalizing on the unique gait patterns exhibited by individuals. This study delineates the development and implementation of a deep learning model, which was trained on a set of extracted features. These features, including mean, variance, standard deviation, kurtosis, and skewness, were derived from accelerometer and gyroscope data, serving as descriptors of users' gait patterns for the deep learning model. The model's performance was evaluated based on its ability to classify and authenticate users accurately using these features. For the purpose of this study, twelve participants were enlisted, with sensors affixed to their back hip and right ankle to collect the requisite accelerometer and gyroscope data. The experimental results were highly promising, with the model achieving an exceptional accuracy rate of 99% in authenticating users. These findings underscore the potential of PACE as a viable alternative to conventional machine learning methods for gait authentication. The implications of this research are far-reaching, with potential applications spanning a multitude of scenarios where security is of paramount importance.more » « less
-
Abstract This study investigated the effect of Transcutaneous Electrical Nerve Stimulation (TENS) for fibromyalgia-like symptoms including chronic widespread musculoskeletal pain, fatigue, and/or gait impairment in twenty-five individuals with long-COVID. Participants were randomized to a high dose (intervention group, IG) or low dose (placebo group, PG) TENS device. Both groups received daily 3–5 h of TENS therapy for 4-weeks. The Brief Pain Inventory assessed functional interference from pain (BPI-I), and pain severity (BPI-S). The global fatigue index (GFI) assessed functional interference from fatigue. Wearable technology measured gait parameters during three 30-feet consecutive walking tasks. At 4-weeks, the IG exhibited a greater decrease in BPI-I compared to the PG (mean difference = 2.61,p = 0.008), and improved in gait parameters including stride time (4-8%, test condition dependent), cadence (4-10%, depending on condition), and double-support phase (12% in dual-task) when compared to baseline. A sub-group meeting the 2010 American College of Rheumatology Fibromyalgia diagnostic criteria undergoing high-dose TENS showed GFI improvement at 4-weeks from baseline (mean change = 6.08,p = 0.005). Daily TENS therapy showed potential in reducing functional interference from pain, fatigue, and gait alterations in long-COVID individuals. The study’s limited power could affect the confirmation of certain observations. Extending the intervention period may improve treatment effectiveness.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/technologies11010018
