Nature exposure impacts affect and brain activation in health and major depression

 Did your therapists ever get you outside or were you resigned to the physical therapy gym? Did they even mention the benefits of forest bathing or blue and green spaces? NO? So, you don't have a functioning therapist, do you?

Shit, this was known a decade ago and your mentors and senior researchers were TOTALLY INCOMPETENT in not knowing about forest bathing?

forest bathing (30 posts to September 2015)

green spaces (6 posts to January 2019)

blue space (9 posts to May 2016)

Nature exposure impacts affect and brain activation in health and major depression

Author links open overlay panel, , , 

https://doi.org/10.1016/j.jenvp.2025.102730Get rights and content

Under a Creative Commons license

Open access

Highlights

• •
  Nature exposure reduces negative affect compared to urban exposure.
• •
  Nature compared to urban exposure reduces activation of the left prefrontal cortex.
• •
  Depressive symptoms seem to not impair the potential of nature's affective benefits.
• •
  A focus on accessible green spaces could preserve mental health and well-being.

Abstract

Considerable research attention has been devoted to the negative impact of urban compared to natural environments on mental health and well-being. Recent studies have addressed the neurobiological underpinnings but have mostly focused on post-hoc effects. How brain activation is acutely changed during nature exposure has barely been investigated. This study sought to assess how affect and cortical activation patterns in a natural environment differ from those in an urban area and how this is influenced by depressive symptoms. To this end, portable functional near-infrared spectroscopy (fNIRS) was used in a 16x16 optode layout to measure cortical activation during a 10-min stay in either a natural or urban environment in 16 patients with major depression and 16 healthy controls. Effects on affect and cognition were explored using questionnaires. Across the whole sample, nature exposure reduced negative affect. When fNIRS channels were allocated to anatomically informed regions of interest, we found decreased spontaneous neural activity in the left ventrolateral prefrontal cortex (PFC) and the right superior temporal gyrus during nature compared to urban exposure, albeit uncorrected for multiple comparisons. Using a data-driven approach, cluster-based permutation testing confirmed a reduced activation of the left PFC during nature exposure. Brain activation remained uninfluenced by depression or antidepressant medication, but depressive symptoms correlated with a reduction of negative affect. Given the limited sample size, these data should be treated as exploratory and only preliminary evidence to suggest that time spent in nature might reduce the strain on brain regions involved in regulating negative emotions and social stress, largely unaffected by depression. These findings warrant replication in larger, future studies.

Recovr Glove: Accessible Hand Exoskeleton for Stroke Rehabilitation and Everyday Aid

 

Which glove is your therapist already using on you? NONE? So, you don't have a functioning therapist either along with your non-functioning doctor? Ask for a comparison analysis so you can choose the best one for recovery. YOUR CHOICE! Not your doctors or your therapists!

• glove (101 posts to October 2011)

Recovr Glove: Accessible Hand Exoskeleton for Stroke Rehabilitation and Everyday Aid

Sharon Zheng; Nova Meng

All Authors

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Abstract

Document Sections

• I.
  Introduction
• II.
  Recovr Glove System Design
• III.
  Recovr Glove Performance Evaluation
• IV.
  Discussion
• V.
  Conclusion

Authors

Figures

References

Keywords

Footnotes

Abstract:

Each year, over 7 million stroke survivors experience upper limb dysfunction, often losing the ability to perform essential hand movements like finger extension and contraction for grasping. Exoskeletons offer a promising solution for rehabilitating gross hand function, providing controlled, repetitive movements and personalized exercises. However, existing models struggle with affordability, usability, and integration into daily life. This paper presents Recovr Glove, a cost-effective, user-friendly exoskeleton glove aimed at improving gross hand function recovery. Recovr features passive extension and active contraction mechanisms, adaptable to varying levels of spasticity. By capturing surface electromyography (sEMG) signals from the flexor digitorum profundus muscle, responsible for grasping, it enables cognitive control of hand movements. We evaluated the Recovr Glove across four parameters: grip strength, extension strength, contraction angle, and object grasping. In motor response testing, Recovr achieved 95% accuracy, with reliable rotation and an average delay of 0.49 seconds ±0.41. During contraction tests, the grasp strength was an average of 55.21± 5.68 N. Extension testing, using a spring scale on each finger, showed an adjustable average force range of 0.1 to 5.5 N for the pinky and from 0.7 to 7.7 N for the thumb, depending on rubber band tension.

Published in: 2025 International Conference On Rehabilitation Robotics (ICORR)

Date of Conference: 12-16 May 2025

Date Added to IEEE Xplore: 11 July 2025

ISBN Information:

ISSN Information:

PubMed ID: 40644289

Efficacy of repeated peripheral magnetic stimulation on upper limb motor function after stroke: a systematic review and meta-analysis of randomized controlled trials

 

 Didn't your competent? doctor start using this a decade ago? Oh, I guess you don't have a functioning stroke doctor or therapists, do you? Why haven't they been fired yet?

• rPMS (9 posts to December 2013)

Efficacy of repeated peripheral magnetic stimulation on upper limb motor function after stroke: a systematic review and meta-analysis of randomized controlled trials

Defu Liao¹Ziyan He¹Shichang Yan¹Qipei Ji¹Yuanlin Li¹Yuyuan Tu¹Zihao Zhou¹Shuangchun Ai^2*

• ¹School of Health and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
• ²Department of Rehabilitation, Mianyang Hospital of Traditional Chinese Medicine, Mianyang, China

Background: Post-stroke patients with upper motor neuron lesions have limited motor function in the upper limbs, and spasticity occurs in the limbs, thus affecting functional recovery and activities of daily living. Repetitive peripheral magnetic stimulation (rPMS) is a non-invasive treatment often used in clinical rehabilitation. Recent studies have shown that it can reduce spasticity and improve motor function in patients.

Objective: This study aimed to evaluate the effectiveness of rPMS on upper limb motor function and spasticity in stroke patients by meta-analysis.

Materials and methods: Randomized controlled trials (RCTs) of rPMS in post-stroke patients were searched in PubMed, Embase, Cochrane Library, Web of Science, and Clinical Trials. Databases from the date of creation to 25 August 2024 were evaluated using the Cochrane Collaboration tool. Methodological quality was assessed using the Cochrane Collaboration tools, and meta-analyses were performed using RevMan (version 5.4) and Stata (version 14.0).

Results: A total of 8 studies were included. RPMS improved patients’ FMA-UE scores compared with controls (MD = 3.34, 95% CI = [0.53, 6.15], p = 0.02 < 0.05). RPMS also reduced spasticity (MD = −0.66, 95% CI = [−1.16, −0.15], p = 0.01 < 0.05) and increased patients’ ability to live independently (MD = 0.85, 95% CI = [0.19, 1.51], p = 0.01 < 0.05). Subgroup analyses showed that the efficacy of treatment frequency ≤ 20 Hz was better than that of frequency > 20 Hz; the treatment time using 15–20 min was more effective than using 30 min; and the application of round coil treatment was more effective than other types of coils.

Conclusion: The results suggest that if rPMS is used in post-stroke patients, their upper limb motor function and spasticity may improve.(NOT GOOD ENOUGH! Survivors want full recovery! Since this didn't do that it was a failure!) However, the number of studies is small, and further research is needed to extend the current analysis results.

Systematic review registration: https://www.crd.york.ac.uk/prospero/, CRD42024584040.

1 Introduction

Stroke, being one of the more prevalent diseases globally, causes severe distress to patients and their families in terms of quality of life, finances, and man-hours. Globally, stroke is the second leading cause of death, accounting for 11.6% of all deaths (1). At the same time, the incidence of stroke is getting younger, which may be related to modern advanced neuroimaging or the dietary and work habits of young people (2). A range of complications can exist after stroke, including dysphagia, impaired consciousness, upper limb motor dysfunction, and cognitive dysfunction (3). If not treated effectively, upper limb motor dysfunction will seriously affect the patient’s daily life activities and cause inconvenience.

The treatment of upper limb motor dysfunction after stroke is based on adaptation or plasticity of the brain after the injury through the practice of specific tasks, medications, robotic trainers, and other methods of enhancing motor learning (4). Improvements in motor function can be achieved using Constraint Induced Movement Therapy (CIMT), which is an operant approach to progressively shape functionally more useful movements using a set of standardized tasks for reaching, grasping, and pinching (5) or by injecting Botulinum Toxin Type A (BoNT-A) to reduce spasticity in the patient’s limbs (6). In recent years, non-invasive stimulation (neuromuscular electrical stimulation, transcranial direct current stimulation, repetitive transcranial magnetic stimulation, and transcutaneous electrical nerve stimulation) has been applied to improve motor function after stroke (7, 8). However, we found that the use of repeated peripheral magnetic stimulation (rPMS) was rare due to unknown parameters and uncertainty about the stimulation site (9).

RPMS is the use of time-varying pulsed magnetic fields of a certain intensity to stimulate excitable tissues, thereby generating induced currents within the tissues, which pass through the nerve cell membranes and enter the axons, resulting in a change in cell membrane potential (10). When the intensity of the stimulus exceeds the cellular threshold, it causes the cell to depolarise to generate an action potential, which in turn causes the muscle to contract (11). Different parameters are applied to reduce pain or promote sensorimotor recovery (12, 13). Impairment of proprioceptive inputs may lead to slower recovery of motor function after stroke (14), one way to restore motor function in patients seems to be to enhance their proprioceptive stimulation. RPMS activates the remodeling of neural tissue in the brain by stimulating proprioceptive inputs, which in turn improves motor function (15). RPMS provides proprioceptive input to the CNS (central nervous system) in two different ways (16), one is direct activation: direct activation of sensorimotor nerve fibers through cis and transduction. The other is indirect activation: indirect activation through mechanoreceptors (class Ia, Ib, and II muscle fibers) during muscle contraction and relaxation. However, the preferential recruitment of cutaneous and proprioceptive afferents over nerves and muscles by rPMS remains controversial (9). There is evidence that the use of rPMS reduces spasticity on the affected side and increases sensory function on the hemiplegic side of the patient (17, 18). RPMS is a painless, non-invasive treatment that has negligible side effects. Suzuki et al. (19) applied rPMS to a male Wistar rat animal model and found that the use of rPMS may not produce damage to the muscles at the application site. Meanwhile, compared with conventional electrical stimulation, rPMS has the advantages of deeper depth and stronger stimulation force.

Although a meta-analysis by Momosaki et al. (20) showed improvement in upper limb spasticity in patients treated with rPMS, there was no statistically significant improvement in upper limb motor function in patients. This meta-analysis aimed to derive the feasibility of rPMS to improve upper limb motor function by analyzing the improvement of upper limb motor function in patients treated with rPMS as well as subgroup analyses at different frequencies, with different coil models, time of stimulation use, and length of post-stroke disease cycle, and to conclude on potentially appropriate therapeutic parameters.

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Cross-step detection using center-of-pressure based algorithm for real-time applications

 What OBJECTIVE method is your doctor and therapist using to determine EXACTLY your gait disabilities? So they can provide EXACT REHAB PROTOCOLS! Oh, you don't have a functioning stroke doctor or therapist because they are doing nothing to objectively determine your problems or have fixes for them? So why are you seeing them if they are that incompetent?

Cross-step detection using center-of-pressure based algorithm for real-time applications

• Matjaž Zadravec &
• Zlatko Matjačić 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 161 (2024) Cite this article

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Abstract

Background

Gait event detection is crucial for assessment, evaluation and provision of biofeedback during rehabilitation of walking. Existing online gait event detection algorithms mostly rely on add-on sensors, limiting their practicality. Instrumented treadmills offer a promising alternative by utilizing the Center of Pressure (CoP) signal for real-time gait event detection. However, current methods have limitations, particularly in detecting cross-step events during perturbed walking conditions.

Methods

We present and validate a CoP-based algorithm to detect gait events and cross-steps in real-time, which combines thresholding and logic techniques. The algorithm was evaluated on CoP datasets from healthy participants (age range 21–61 years), stroke survivors (age range 20–67 years), and people with unilateral transtibial amputation (age range 28–63 years) that underwent perturbation-based balance assessments, encompassing different walking speeds. Detected gait events from a simulated real-time processing operation were compared to offline identified counterparts in order to present related temporal absolute mean errors (AME) and success rate.

Results

The proposed algorithm demonstrated high accuracy in detecting gait events during native gait, as well as cross-step events during perturbed walking conditions. It successfully recognized the majority of cross-steps, with a detection success rate of 94%. However, some misclassifications or missed events occurred, mainly due to the complexity of cross-step events. AME for heel strikes (HS) during native gait and cross-step events averaged at 78 ms and 64 ms respectively, while toe off (TO) AME were 126 ms and 111 ms respectively. A statistically significant difference in the algorithm's success rate score in detecting gait events during cross-step intervals was observed across various walking speeds in a sample of 12 healthy participants, while there was no significant difference among groups.

Conclusion

The proposed algorithm represents an advancement in gait event detection on instrumented treadmills. By leveraging the CoP signal, it successfully identifies gait events and cross-steps in the simulated real-time processing operation, providing valuable insights into human locomotion. The algorithm's ability to accommodate diverse CoP patterns enhance its applicability to a wide range of individuals and gait characteristics. The algorithm's performance was consistent across different populations, suggesting its potential for diverse clinical and research settings, particularly in the domains of gait analysis and rehabilitation practices.

Introduction

Gait analysis plays a crucial role in understanding human locomotion and assessing the effectiveness of rehabilitation therapies [1,2,3]. Accurate and reliable real-time detection of gait events, such as heel strikes (HS) and toe offs (TO), is essential for monitoring various gait parameters as well as for providing real-time biofeedback during gait training. Such immediate feedback allows researchers and clinicians to evaluate data in real-time, as well as to make on-the-fly adjustments and interventions. In rehabilitation, it is crucial to tailor exercises to an individual's gait pattern and abilities to maximize the effectiveness of the training. This ensures that the exercises are both appropriate and beneficial for the individual's specific needs. Real-time gait event detection also plays a key role in prosthetics, enabling responsive control for artificial limbs and resulting in a more natural walking experience. Additionally, it is essential for fall detection systems for the elderly, triggering timely preventive measures. Overall, real-time gait event detection enhances customization and optimization in rehabilitation programs, ensuring effective and personalized treatment. Conventionally, online gait event detection algorithms rely on various sensors (Inertial Measurement Units – IMU, pressure insoles, angular sensors or optical tracking systems) attached to the lower limbs or body to capture the kinematics of movement [4,5,6,7]. Particularly, IMU systems, whether using a single or multiple units, have the capability to detect gait events, though their success depends on several factors such as the quality of the IMU units, signal processing techniques, individual's gait behaviour or suitable sensor positioning [8, 9]. However, these methods can be often burdensome for participants, have potential issues with synchronization and often limit the practicality of the procedure setup, especially for everyday use in a clinical environment [10].

In recent years, instrumented treadmills equipped with force transducers to measure Center of Pressure (CoP) during walking, including single or split-belt treadmill variations, have gained popularity as a valuable tool for gait analysis and training [11,12,13,14]. Here, the CoP represents the point where the vertical ground reaction force is applied and can provide valuable insights into gait dynamics [15,16,17,18]. Leveraging the CoP signal, researchers have developed real-time algorithms for detecting gait events and gait subphases without the need for additional human add-on sensors [16, 19,20,21]. Additionally, force signals from the instrumented split-belt treadmills have been used by the researchers to employ a thresholding method based on force data for detecting gait events [22, 23]. However, it's important to note that this technique is applicable solely during native (unperturbed) gait, where each leg makes contact with each belt of the treadmill. Conversely, in the case of a cross-step, both feet are in contact with only one belt, rendering the conventional method ineffective.

Two related studies have examined the CoP-based algorithm, one involving healthy participants and the other focusing on subjects with amputations [16, 20]. In the both studies, participants performed their native gait, revealing asymmetrical butterfly-shaped CoP signals assessed in people with amputations [20] as opposed to the unimpaired gait in healthy subjects [16]. The acquired signals were analyzed to assess specific gait characteristics such as step length, width, time, and durations of double and single support. However, these studies did not include analysis and evaluation of perturbed walking conditions.

One particular challenge for real-time gait event detection algorithms is accurate identification of stepping responses during perturbation-based balance training (PBT). PBT has emerged as a valuable approach for improving balance control and reducing the risk of falls in the elderly and neurologically impaired [24,25,26]. During PBT, individuals experience controlled perturbations that challenge their stability, leading to different reactive balance strategies. These strategies involve adjustments in step length and width to regain dynamic stability. Among these adjustments, cross-steps have been identified as reactive balance responses following outward perturbations, where individuals cross their legs during the gait cycle to stabilize the body and restore equilibrium [27,28,29]. Therefore, accurately detecting cross-steps poses a significant challenge to traditional algorithms, as cross-steps disrupt the expected "butterfly-shaped" pattern of CoP movement following externally or internally elicited perturbation [27, 28]. While these studies have explored gait abnormalities caused by pathology or varying step widths and lengths, the specific scenario of crossing legs during walking has not been thoroughly investigated. Consequently, there is a gap in contemporary methods that can reliably examine cross-step events. Currently, the lack of real-time algorithms capable of detecting and quantifying cross-steps without the need for wearable sensors is a notable limitation hindering the use of real-time biofeedback during gait training.

The aim of this study was to develop a real-time algorithm that utilizes the CoP signal from a single-belt instrumented treadmill to accurately detect HS and TO events during both native and perturbed gait, specifically addressing cross-step events. We conducted extensive experiments to evaluate the algorithm's reliability and accuracy on diverse populations, including healthy participants, subjects with unilateral transtibial amputations, and individuals after stroke, across different walking speeds.

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