Title: Exploring Training Methodologies Towards the Improvement of Elderly Balance
The purpose of this study was to investigate the effects of utilizing sensory (i.e., vision and touch), as well as static and dynamic base of support training on the balance of senior participants aged 60–80 years old. For each participant, there were several weeks of training, two sessions per week and assessments every two weeks. Training included walking and standing exercises on a hard surface, compliant and stiffer foam walking and standing balance training, and navigating obstacles. Within each session, to modify vision, all training included eyes-open and closed. Further, there were increases in training difficulty as the sessions progressed.
It was observed that training over several weeks resulted in increases in stability, as observed by the decreases in Balance Error Scoring System (BESS) assessment results. However, increases in balance confidence, as observed by the Activities-Specific Balance Confidence (ABC) scale were less certain in this healthy elderly (or senior) population. It is an interesting and positive finding that, in doing relatively simple, but targeted exercises and training, senior individuals can have moderate improvements in their balance and, perhaps ultimately, reduce their fall-risk.
Thompson, Lara A.; Badache, Mehdi; Brusamolin, Joao Augusto; Savadkoohi, Marzieh; Guise, Jelani; de Paiva, Gabriel Velluto; Suh, Pius; Sanchez Guerrero, Pablo; Shetty, Devdas(
, Journal of Aging Research)
Mulherkar, Shalaka
(Ed.)
Increasing balance confidence in older individuals is important towards improving their quality of life and reducing activity avoidance. Here, we investigated if balance confidence (perceived ability) and balance performance (ability) in older adults were related to one another and would improve after balance training. The relationship of balance confidence in conjunction with balance performance for varied conditions (such as limiting vision, modifying somatosensory cues, and also base of support) was explored. We sought to determine if balance confidence and ability, as well as their relationship, could change after several weeks of training. Twenty-seven healthy participants were trained for several weeks during standing and walking exercises. In addition, seven participants with a higher risk of imbalance leading to falls (survivors of stroke) were also trained. Prior to and after training, balance ability and confidence were assessed via the Balance Error Scoring System (BESS) and Activities Specific Balance Confidence (ABC) Scale, respectively. Both groups showed improvements in balance abilities (i.e., BESS errors significantly decreased after training). Balance confidence was significantly higher in the healthy group than in the stroke group; however, ABC results reflected that balance confidence did not significantly increase after training for each. The correlations between balance ability and balance confidence were explored. Encouragingly, healthy participants displayed a negative correlation between BESS errors and ABC (i.e., enhancements in balance confidence (increases in ABC Scale results) were related to improvements in balance ability (decreases in BESS errors)). For the stroke participants, despite improvements in balance ability, our results showed that there was no relation to balance confidence (i.e., no correlation between BESS errors and ABC) in this group.
For the rapidly growing aging demographic worldwide, robotic training methods could be impactful towards improving balance critical for everyday life. Here, we investigated the hypothesis that non-bodyweight supportive (nBWS) overground robotic balance training would lead to improvements in balance performance and balance confidence in older adults. Sixteen healthy older participants (69.7 ± 6.7 years old) were trained while donning a harness from a distinctive NaviGAITor robotic system. A control group of 11 healthy participants (68.7 ± 5.0 years old) underwent the same training but without the robotic system. Training included 6 weeks of standing and walking tasks while modifying: (1) sensory information (i.e., with and without vision (eyes-open/closed), with more and fewer support surface cues (hard or foam surfaces)) and (2) base-of-support (wide, tandem and single-leg standing exercises). Prior to and post-training, balance ability and balance confidence were assessed via the balance error scoring system (BESS) and the Activities specific Balance Confidence (ABC) scale, respectively. Encouragingly, results showed that balance ability improved (i.e., BESS errors significantly decreased), particularly in the nBWS group, across nearly all test conditions. This result serves as an indication that robotic training has an impact on improving balance for healthy aging individuals.
Porciuncula, Franchino; Baker, Teresa C.; Arumukhom Revi, Dheepak; Bae, Jaehyun; Sloutsky, Regina; Ellis, Terry D.; Walsh, Conor J.; Awad, Louis N.(
, Frontiers in Neurorobotics)
null
(Ed.)
Background: Soft robotic exosuits can facilitate immediate increases in short- and long-distance walking speeds in people with post-stroke hemiparesis. We sought to assess the feasibility and rehabilitative potential of applying propulsion-augmenting exosuits as part of an individualized and progressive training program to retrain faster walking and the underlying propulsive strategy. Methods: A 54-yr old male with chronic hemiparesis completed five daily sessions of Robotic Exosuit Augmented Locomotion (REAL) gait training. REAL training consists of high-intensity, task-specific, and progressively challenging walking practice augmented by a soft robotic exosuit and is designed to facilitate faster walking by way of increased paretic propulsion. Repeated baseline assessments of comfortable walking speed over a 2-year period provided a stable baseline from which the effects of REAL training could be elucidated. Additional outcomes included paretic propulsion, maximum walking speed, and 6-minute walk test distance. Results: Comfortable walking speed was stable at 0.96 m/s prior to training and increased by 0.30 m/s after training. Clinically meaningful increases in maximum walking speed (Δ: 0.30 m/s) and 6-minute walk test distance (Δ: 59 m) were similarly observed. Improvements in paretic peak propulsion (Δ: 2.80 %BW), propulsive power (Δ: 0.41 W/kg), and trailing limb angle (Δ: 6.2 degrees) were observed at comfortable walking speed ( p 's < 0.05). Likewise, improvements in paretic peak propulsion (Δ: 4.63 %BW) and trailing limb angle (Δ: 4.30 degrees) were observed at maximum walking speed ( p 's < 0.05). Conclusions: The REAL training program is feasible to implement after stroke and capable of facilitating rapid and meaningful improvements in paretic propulsion, walking speed, and walking distance.
Thompson, Lara A.; Savadkoohi, Marzieh; de Paiva, Gabriel Velluto; Augusto Renno Brusamolin, Joao; Guise, Jelani; Suh, Pius; Guerrero, Pablo Sanchez(
, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))
null
(Ed.)
With the massive growth of the aging population worldwide, of utmost importance is reducing falls. Critical to reducing fall risk is one's ability to weight incoming sensory information towards maintaining balance. The purpose of this research was to investigate if simple, targeted sensory training on aging individuals (50 - 80 years old), including twelve healthy and eight individuals with chronic stroke, could improve their balance. Repeated sensory training targeted visual (via eyes-open/closed) and somatosensory inputs (via light touch to the fingertip as well as hard, soft foam, and hard foam support surfaces to the feet) during standing and dynamic base-of-support (BOS) exercises. Study participants underwent six weeks of training. Prior to and post training, standing balance was assessed via a simple, clinical measure: the balance error scoring system (BESS). Following several weeks of training, participants showed significant improvements in BESS errors: healthy participants for small BOS with limited somatosensory information (i.e., tandem and single-leg standing on foam) and participants with stroke in all conditions.Clinical Relevance- This research study demonstrated that simple, accessible exercises, can positively impact balance in the aging population, a pressing need.
Rahman, Safwanur
Hamid(
, IEEE Signal Processing in Medicine and Biology Symposium (SPMB))
Obeid, Iyad; Selesnick, Ivan; Picone, Joseph
(Ed.)
The Temple University Hospital Seizure Detection Corpus (TUSZ) [1] has been in distribution since April 2017. It is a subset of the TUH EEG Corpus (TUEG) [2] and the most frequently requested corpus from our 3,000+ subscribers. It was recently featured as the challenge task in the Neureka 2020 Epilepsy Challenge [3]. A summary of the development of the corpus is shown below in Table 1.
The TUSZ Corpus is a fully annotated corpus, which means every seizure event that occurs within its files has been annotated. The data is selected from TUEG using a screening process that identifies files most likely to contain seizures [1]. Approximately 7% of the TUEG data contains a seizure event, so it is important we triage TUEG for high yield data. One hour of EEG data requires approximately one hour of human labor to complete annotation using the pipeline described below, so it is important from a financial standpoint that we accurately triage data.
A summary of the labels being used to annotate the data is shown in Table 2. Certain standards are put into place to optimize the annotation process while not sacrificing consistency. Due to the nature of EEG recordings, some records start off with a segment of calibration. This portion of the EEG is instantly recognizable and transitions from what resembles lead artifact to a flat line on all the channels. For the sake of seizure annotation, the calibration is ignored, and no time is wasted on it. During the identification of seizure events, a hard “3 second rule” is used to determine whether two events should be combined into a single larger event. This greatly reduces the time that it takes to annotate a file with multiple events occurring in succession. In addition to the required minimum 3 second gap between seizures, part of our standard dictates that no seizure less than 3 seconds be annotated. Although there is no universally accepted definition for how long a seizure must be, we find that it is difficult to discern with confidence between burst suppression or other morphologically similar impressions when the event is only a couple seconds long. This is due to several reasons, the most notable being the lack of evolution which is oftentimes crucial for the determination of a seizure.
After the EEG files have been triaged, a team of annotators at NEDC is provided with the files to begin data annotation. An example of an annotation is shown in Figure 1. A summary of the workflow for our annotation process is shown in Figure 2. Several passes are performed over the data to ensure the annotations are accurate. Each file undergoes three passes to ensure that no seizures were missed or misidentified. The first pass of TUSZ involves identifying which files contain seizures and annotating them using our annotation tool. The time it takes to fully annotate a file can vary drastically depending on the specific characteristics of each file; however, on average a file containing multiple seizures takes 7 minutes to fully annotate. This includes the time that it takes to read the patient report as well as traverse through the entire file.
Once an event has been identified, the start and stop time for the seizure is stored in our annotation tool. This is done on a channel by channel basis resulting in an accurate representation of the seizure spreading across different parts of the brain. Files that do not contain any seizures take approximately 3 minutes to complete. Even though there is no annotation being made, the file is still carefully examined to make sure that nothing was overlooked. In addition to solely scrolling through a file from start to finish, a file is often examined through different lenses. Depending on the situation, low pass filters are used, as well as increasing the amplitude of certain channels. These techniques are never used in isolation and are meant to further increase our confidence that nothing was missed. Once each file in a given set has been looked at once, the annotators start the review process. The reviewer checks a file and comments any changes that they recommend. This takes about 3 minutes per seizure containing file, which is significantly less time than the first pass. After each file has been commented on, the third pass commences. This step takes about 5 minutes per seizure file and requires the reviewer to accept or reject the changes that the second reviewer suggested. Since tangible changes are made to the annotation using the annotation tool, this step takes a bit longer than the previous one. Assuming 18% of the files contain seizures, a set of 1,000 files takes roughly 127 work hours to annotate.
Before an annotator contributes to the data interpretation pipeline, they are trained for several weeks on previous datasets. A new annotator is able to be trained using data that resembles what they would see under normal circumstances. An additional benefit of using released data to train is that it serves as a means of constantly checking our work. If a trainee stumbles across an event that was not previously annotated, it is promptly added, and the data release is updated. It takes about three months to train an annotator to a point where their annotations can be trusted. Even though we carefully screen potential annotators during the hiring process, only about 25% of the annotators we hire survive more than one year doing this work.
To ensure that the annotators are consistent in their annotations, the team conducts an interrater agreement evaluation periodically to ensure that there is a consensus within the team. The annotation standards are discussed in Ochal et al. [4]. An extended discussion of interrater agreement can be found in Shah et al. [5].
The most recent release of TUSZ, v1.5.2, represents our efforts to review the quality of the annotations for two upcoming challenges we hosted: an internal deep learning challenge at IBM [6] and the Neureka 2020 Epilepsy Challenge [3]. One of the biggest changes that was made to the annotations was the imposition of a stricter standard for determining the start and stop time of a seizure. Although evolution is still included in the annotations, the start times were altered to start when the spike-wave pattern becomes distinct as opposed to merely when the signal starts to shift from background. This cuts down on background that was mislabeled as a seizure. For seizure end times, all post ictal slowing that was included was removed.
The recent release of v1.5.2 did not include any additional data files. Two EEG files had been added because, originally, they were corrupted in v1.5.1 but were able to be retrieved and added for the latest release. The progression from v1.5.0 to v1.5.1 and later to v1.5.2, included the re-annotation of all of the EEG files in order to develop a confident dataset regarding seizure identification. Starting with v1.4.0, we have also developed a blind evaluation set that is withheld for use in competitions.
The annotation team is currently working on the next release for TUSZ, v1.6.0, which is expected to occur in August 2020. It will include new data from 2016 to mid-2019. This release will contain 2,296 files from 2016 as well as several thousand files representing the remaining data through mid-2019. In addition to files that were obtained with our standard triaging process, a part of this release consists of EEG files that do not have associated patient reports. Since actual seizure events are in short supply, we are mining a large chunk of data for which we have EEG recordings but no reports. Some of this data contains interesting seizure events collected during long-term EEG sessions or data collected from patients with a history of frequent seizures. It is being mined to increase the number of files in the corpus that have at least one seizure event. We expect v1.6.0 to be released before IEEE SPMB 2020.
The TUAR Corpus is an open-source database that is currently available for use by any registered member of our consortium. To register and receive access, please follow the instructions provided at this web page: https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml. The data is located here: https://www.isip.piconepress.com/projects/tuh_eeg/downloads/tuh_eeg_artifact/v2.0.0/.
Thompson, Lara A., Brusamolin, Joao Augusto, Guise, Jelani, Badache, Mehdi, Estrada, Sandy Collado, Behera, Lonika, Savadkoohi, Marzieh, Coombs, Tyra, Guerrero, Pablo Sanchez, and Shetty, Devdas. Exploring Training Methodologies Towards the Improvement of Elderly Balance. Retrieved from https://par.nsf.gov/biblio/10315767. Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition Volume 3: Biomedical and Biotechnology Engineering. Web. doi:10.1115/IMECE2018-86815.
Thompson, Lara A., Brusamolin, Joao Augusto, Guise, Jelani, Badache, Mehdi, Estrada, Sandy Collado, Behera, Lonika, Savadkoohi, Marzieh, Coombs, Tyra, Guerrero, Pablo Sanchez, & Shetty, Devdas. Exploring Training Methodologies Towards the Improvement of Elderly Balance. Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition, Volume 3: Biomedical and Biotechnology Engineering (). Retrieved from https://par.nsf.gov/biblio/10315767. https://doi.org/10.1115/IMECE2018-86815
Thompson, Lara A., Brusamolin, Joao Augusto, Guise, Jelani, Badache, Mehdi, Estrada, Sandy Collado, Behera, Lonika, Savadkoohi, Marzieh, Coombs, Tyra, Guerrero, Pablo Sanchez, and Shetty, Devdas.
"Exploring Training Methodologies Towards the Improvement of Elderly Balance". Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition Volume 3: Biomedical and Biotechnology Engineering (). Country unknown/Code not available. https://doi.org/10.1115/IMECE2018-86815.https://par.nsf.gov/biblio/10315767.
@article{osti_10315767,
place = {Country unknown/Code not available},
title = {Exploring Training Methodologies Towards the Improvement of Elderly Balance},
url = {https://par.nsf.gov/biblio/10315767},
DOI = {10.1115/IMECE2018-86815},
abstractNote = {The purpose of this study was to investigate the effects of utilizing sensory (i.e., vision and touch), as well as static and dynamic base of support training on the balance of senior participants aged 60–80 years old. For each participant, there were several weeks of training, two sessions per week and assessments every two weeks. Training included walking and standing exercises on a hard surface, compliant and stiffer foam walking and standing balance training, and navigating obstacles. Within each session, to modify vision, all training included eyes-open and closed. Further, there were increases in training difficulty as the sessions progressed. It was observed that training over several weeks resulted in increases in stability, as observed by the decreases in Balance Error Scoring System (BESS) assessment results. However, increases in balance confidence, as observed by the Activities-Specific Balance Confidence (ABC) scale were less certain in this healthy elderly (or senior) population. It is an interesting and positive finding that, in doing relatively simple, but targeted exercises and training, senior individuals can have moderate improvements in their balance and, perhaps ultimately, reduce their fall-risk.},
journal = {Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition},
volume = {Volume 3: Biomedical and Biotechnology Engineering},
author = {Thompson, Lara A. and Brusamolin, Joao Augusto and Guise, Jelani and Badache, Mehdi and Estrada, Sandy Collado and Behera, Lonika and Savadkoohi, Marzieh and Coombs, Tyra and Guerrero, Pablo Sanchez and Shetty, Devdas},
}
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