BACKGROUND Movement analysis in the clinical setting is frequently restricted to observational methods to inform clinical decision making, which has limited accuracy. Fixed-site optical expensive movement analysis laboratories provide ‘gold-standard’ kinematic measurements, however they are rarely accessed for routine clinical use. Wearable inertial measurement units (IMUs) have been demonstrated as comparable, inexpensive and portable movement analysis toolkit. MoJoXlab has therefore been developed to work with generic wearable IMUs. However, before using MoJoXlab in clinical practice there is a need to establish its validity in participants with and without knee conditions across a range of tasks with varying complexity. OBJECTIVE This paper presents the validation of MoJoXlab software for using generic wearable IMUs in calculating hip, knee and ankle joint angle measurements in the sagittal, frontal and transverse planes, for walking, squatting and jumping in healthy participants and those with anterior cruciate ligament reconstruction. METHODS Movement data were collected from 27 healthy participants and 20 participants with Anterior Cruciate Ligament (ACL) reconstruction. In each case, participants wore seven ‘MTw2’ IMUs to monitor their movement in walking, jumping and squatting tasks. Hip, knee and ankle joint angles were calculated in the sagittal, frontal and transverse plane using two different software packages: Xsens’s validated proprietary MVN Analyze, and MoJoXlab. Results were validated by comparing the generated waveforms, cross-correlation (CC) and normalized root mean square error (NRMSE) values. RESULTS Across all joints and activities, for both healthy and ACL reconstruction data, the cross-correlation and normalized root mean square error for the sagittal plane are: 0.99 ± 0.01 and 0.042 ± 0.025 respectively; for the frontal plane: 0.88 ± 0.048 and 0.18 ± 0.078; and for the transverse plane (hip and knee joints only): 0.85 ± 0.027 and 0.23 ± 0.065. On comparing results from the two different software systems, the sagittal plane is very highly correlated, with frontal and transverse planes showing strong correlation. CONCLUSIONS This paper demonstrates that non-proprietary software such as MoJoXlab can accurately calculate joint angles for movement analysis applications comparable to proprietary software, for walking, squatting and jumping, in healthy individuals and those following anterior cruciate ligament reconstruction. MoJoXlab can be used with generic wearable IMUs that can provide clinicians accurate objective data when assessing patients’ movement, even when changes are too small to be observed visually. The availability of easy-to-setup, non-proprietary software for calibration, data collection and joint angle calculation has the potential to increase the adoption of wearable IMU sensors in clinical practice, as well as in free living conditions, and may provide wider access to accurate, objective assessment of patients’ progress over time. CLINICALTRIAL
Depletion of fossil fuel and the inability to meet the rising demand of electricity are some drawbacks for the economic development of Bangladesh. Carbon emission done by developed world is also troubling the country. This paper focuses on the potential of micro-hydropower plant in Bangladesh due to its numerous rivers and canals providing off-grid power to the remote areas and also to the areas that are still outside the main grid network. This paper reflects on the current energy scenario in Bangladesh, the need to explore green energy thus proving how the establishment of widespread micro-hydropower plant can help overcome the current power crisis and play a role in the economic progress of the country. The existing potential sites are mentioned and the means to identify new sites are outlined by performing hydrology studies, topographic studies, head calculations, turbine selection, and so forth.
Background Movement analysis in a clinical setting is frequently restricted to observational methods to inform clinical decision making, which has limited accuracy. Fixed-site, optical, expensive movement analysis laboratories provide gold standard kinematic measurements; however, they are rarely accessed for routine clinical use. Wearable inertial measurement units (IMUs) have been demonstrated as comparable, inexpensive, and portable movement analysis toolkits. MoJoXlab has therefore been developed to work with generic wearable IMUs. However, before using MoJoXlab in clinical practice, there is a need to establish its validity in participants with and without knee conditions across a range of tasks with varying complexity. Objective This paper aimed to present the validation of MoJoXlab software for using generic wearable IMUs for calculating hip, knee, and ankle joint angle measurements in the sagittal, frontal, and transverse planes for walking, squatting, and jumping in healthy participants and those with anterior cruciate ligament (ACL) reconstruction. Methods Movement data were collected from 27 healthy participants and 20 participants with ACL reconstruction. In each case, the participants wore seven MTw2 IMUs (Xsens Technologies) to monitor their movement in walking, jumping, and squatting tasks. The hip, knee, and ankle joint angles were calculated in the sagittal, frontal, and transverse planes using two different software packages: Xsens’ validated proprietary MVN Analyze and MoJoXlab. The results were validated by comparing the generated waveforms, cross-correlation (CC), and normalized root mean square error (NRMSE) values. Results Across all joints and activities, for data of both healthy and ACL reconstruction participants, the CC and NRMSE values for the sagittal plane are 0.99 (SD 0.01) and 0.042 (SD 0.025); 0.88 (SD 0.048) and 0.18 (SD 0.078) for the frontal plane; and 0.85 (SD 0.027) and 0.23 (SD 0.065) for the transverse plane (hip and knee joints only). On comparing the results from the two different software systems, the sagittal plane was very highly correlated, with frontal and transverse planes showing strong correlation. Conclusions This study demonstrates that nonproprietary software such as MoJoXlab can accurately calculate joint angles for movement analysis applications comparable with proprietary software for walking, squatting, and jumping in healthy individuals and those following ACL reconstruction. MoJoXlab can be used with generic wearable IMUs that can provide clinicians accurate objective data when assessing patients’ movement, even when changes are too small to be observed visually. The availability of easy-to-setup, nonproprietary software for calibration, data collection, and joint angle calculation has the potential to increase the adoption of wearable IMU sensors in clinical practice, as well as in free living conditions, and may provide wider access to accurate, objective assessment of patients’ progress over time.
Background Huntington disease (HD) is an inherited genetic disorder that results in the death of brain cells. HD symptoms generally start with subtle changes in mood and mental abilities; they then degenerate progressively, ensuing a general lack of coordination and an unsteady gait, ultimately resulting in death. There is currently no cure for HD. Walking cued by an external, usually auditory, rhythm has been shown to steady gait and help with movement coordination in other neurological conditions. More recently, work with other neurological conditions has demonstrated that haptic (ie, tactile) rhythmic cues, as opposed to audio cues, offer similar improvements when walking. An added benefit is that less intrusive, more private cues are delivered by a wearable device that leaves the ears free for conversation, situation awareness, and safety. This paper presents a case study where rhythmic haptic cueing (RHC) was applied to one person with HD. The case study has two elements: the gait data we collected from our wearable devices and the comments we received from a group of highly trained expert physiotherapists and specialists in HD. Objective The objective of this case study was to investigate whether RHC can be applied to improve gait coordination and limb control in people living with HD. While not offering a cure, therapeutic outcomes may delay the onset or severity of symptoms, with the potential to improve and prolong quality of life. Methods The approach adopted for this study includes two elements, one quantitative and one qualitative. The first is a repeated-measures design with three conditions: before haptic rhythm (ie, baseline), with haptic rhythm, and after exposure to haptic rhythm. The second element is an in-depth interview with physiotherapists observing the session. Results In comparison to the baseline, the physiotherapists noted a number of improvements to the participant’s kinematics during her walk with the haptic cues. These improvements continued in the after-cue condition, indicating some lasting effects. The quantitative data obtained support the physiotherapists’ observations. Conclusions The findings from this small case study, with a single participant, suggest that a haptic metronomic rhythm may have immediate, potentially therapeutic benefits for the walking kinematics of people living with HD and warrants further investigation.
We investigated five methylation markers recently linked to body mass index, for their role in the neuropathology of obesity. In neuroimaging experiments, our analysis involving 23 participants showed that methylation levels for the cg07814318 site, which lies within the KLF13 gene, correlated with brain activity in the claustrum, putamen, cingulate gyrus and frontal gyri, some of which have been previously associated to food signaling, obesity or reward. Methylation levels at cg07814318 also positively correlated with ghrelin levels. Moreover, expression of KLF13 was augmented in the brains of obese and starved mice. Our results suggest the cg07814318 site could be involved in orexigenic processes, and also implicate KLF13 in obesity. Our findings are the first to associate methylation levels in blood with brain activity in obesity-related regions, and further support previous findings between ghrelin, brain activity and genetic differences.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.