#gaitanalysis

Thigh-Worn Sensor for Measuring Initial and Final Contact During Gait in a Mobility Impaired Population: Validation Study Background: Measuring free-living gait with wearable sensors has great potential in supporting personalised rehabilitation. There are challenges meeting the accuracy levels of laboratory-based measurements in detecting initial and final contact, particularly in impaired populations. Objective: To test the criterion validity of a novel temporal gait measurement technique, combining the ActivPAL 4+ (PAL Technologies, Glasgow, UK) and the Teager-Kaiser Energy Operator, to measure stance phase duration in chronic stroke survivors through comparison with the Evoke cluster marker system (Vicon, Oxford, UK). Methods: Stroke participants (n=13, mean age = 59 years  14, time since stroke = 1.5 years  0.5) were assessed using the ACTIVPAL 4+ and a motion capture system. Two 10m walk tests were measured, while wearing two ActivPAL 4+ (located on anterior of both thighs) and clusters on the pelvis and ankles from the motion capture system. The Teager-Kaiser Energy Operator signal processing technique was used to extract the stance durations of the ActivPAL 4+, compared with a previously validated method. Results: There was a good agreement (bias: 0.03s, limits of agreement: -0.22 to 0.28s) between the ACTIVPAL 4+ and motion capture system despite a slight underestimation (mean stance time: 0.850s vs. motion capture system: 0.881s). Conclusions: Findings suggest the ACTIVPAL 4+, combined with Teager-Kaiser Energy Operator technique, provides valid stance time measurements when compared laboratory-based systems, supporting its use in free-living gait analysis and feedback during rehabilitation. Limitations include laboratory-only validation and a small population of chronic stroke patients. Future work should explore free-living gait, and larger, and broader, cross section of stroke populations.

4 months ago

New JMIR BioMedEng: Thigh-Worn Sensor for Measuring Initial and Final Contact During Gait in a Mobility Impaired Population: Validation Study #WearableTech #GaitAnalysis #Rehabilitation #StrokeRecovery #MobilityImpairment

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Correction: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study

4 months ago

New in JMIR Aging: Correction: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study #MachineLearning #FrailtyDetection #GaitAnalysis #PhysicalActivity #LongTermCare

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5 months ago

Multi‑Stage MoME Model Boosts Gait‑Based Psychological Trait Prediction

The Multi‑Stage MoME model predicts 17 traits from walking patterns, achieving a weighted F1 score of 37.47 % at the run level and 44.6 % at the subject level. Read more: getnews.me/multi-stage-mome-model-b... #gaitanalysis #psychology

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5 months ago

CARE-PD: Multi‑Site 3D Mesh Dataset for Parkinson's Gait Assessment

CARE‑PD provides the largest public 3D mesh gait dataset, covering nine cohorts at eight centres. Pre‑training cuts MPJPE from 60.8 mm to 7.5 mm and raises macro‑F1 by 17 pts. getnews.me/care-pd-multi-site-3d-me... #carepd #gaitanalysis

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Rafa Aguilar

@raguilar.es

5 months ago

🚀 Last week at CAIP 2025 we presented our paper "What Does Gait Reveal About Health? Investigating Human Motion as an Indicator". Super exciting to share our latest results and connect with so many brilliant people! ✨@ucoava.bsky.social

#CAIP2025 #GaitAnalysis #HealthTech #AI #ComputerVision

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5 months ago

DynSTG-Mamba Withdrawn as Authors Revise Gait Disorder AI Model

The DynSTG-Mamba spatio‑temporal graph model for gait disorder recognition has been withdrawn; authors will revise filtering and validation before resubmission. Read more: getnews.me/dynstg-mamba-withdrawn-a... #dynstgmamba #gaitanalysis #aihealth

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5 months ago

Camera View Impacts Accuracy in 2D Markerless Gait Analysis

In 18 participants, lateral cameras cut step‑length DTW error to 53 mm versus 70 mm frontally and improve sagittal metrics, while frontal views lower trunk‑rotation KLD to 0.09 vs 0.30. getnews.me/camera-view-impacts-accu... #gaitanalysis #cameraangle

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5 months ago

Explainable Gait Abnormality Detection with Dual-Dataset CNN‑LSTM

Researchers introduced a dual‑branch CNN‑LSTM that combines joint trajectories and silhouette images, achieving 98.6% accuracy on held‑out test sets and cited at ICMLA‑2025. Read more: getnews.me/explainable-gait-abnorma... #gaitanalysis #explainableai

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5 months ago

3D Gait Analysis and Pedography Shift Idiopathic Toe Walking Care

3D gait analysis and pedography led 15 of 47 kids with idiopathic toe walking to switch from planned surgery to physiotherapy or observation. Read more: getnews.me/3d-gait-analysis-and-ped... #idiopathictoewalking #gaitanalysis

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5 months ago

Gait Analysis Guide: Unlocking Knee Biomechanics for Clinicians

A September 2025 review shows gait analysis—using cameras or sensors—reveals knee joint angles, moments and alignment, helping detect osteoarthritis and ACL issues. Read more: getnews.me/gait-analysis-guide-unlo... #gaitanalysis #kneebiomechanics

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Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study Background: Frailty affects over 50% of older adults in long-term care (LTC), and early detection is critical due to its potential reversibility. Wearable sensors enable continuous monitoring of gait and physical activity, and machine learning has shown promise in detecting frailty among community-dwelling older adults. However, its applicability in LTC remains underexplored. Furthermore, dynamic gait outcomes (eg, gait stability and symmetry) may offer more sensitive frailty indicators than traditional measures like gait speed, yet their potential remains largely untapped. Objective: This study aimed to evaluate whether frailty in LTC facilities could be effectively identified using machine learning models trained on gait and daily physical activity data derived from a single accelerometer. Methods: This study is a cross-sectional secondary analysis of baseline data from a 2-arm cluster randomized controlled trial. Of the 164 individuals initially enrolled, 51 participants (age: mean 85.0, SD 9.0 years; female: n=24, 47.1%) met the inclusion criteria of completing all assessments required for this study and were included in the final analysis. Frailty status was assessed using the fatigue, resistance, ambulation, incontinence, loss of weight, nutritional approach, and help with dressing (FRAIL-NH) scale. Participants completed a 5-meter walking task while wearing a 3D accelerometer. Following this task, the accelerometer was used to record daily physical activity over approximately 1 week. A total of 34 dynamic and spatial-temporal gait outcomes, 3 physical activity variables, and 6 demographic characteristics were extracted. Five conventional machine learning models were trained to classify frailty status using a leave-one-out cross-validation approach. Model performance was evaluated based on accuracy and the area under the receiver operating characteristic curve. To enhance model interpretability, explainable artificial intelligence techniques were used to identify the most influential predictive outcomes. Results: The extreme gradient boosting model demonstrated the optimal performance with an accuracy of 86.3% and an area under the curve of 0.92. Explainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, and a higher gait symmetry score. Conclusions: Our findings suggest that dynamic gait outcomes may serve as more sensitive indicators of frailty than spatial-temporal gait outcomes (eg, gait speed) in LTC settings, offering valuable insights for enhancing frailty detection and management.

6 months ago

New in JMIR Aging: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study #MachineLearning #FrailtyDetection #LongTermCare #WearableTechnology #GaitAnalysis

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novel - Accurate force & pressure sensors

@novel-artinscience.bsky.social

7 months ago

A unique look at #biomechanics under extreme conditions! A gladiator marathon simulation using the #loadsol pro mlp led by Karen Mickle from the University of Newcastle. We can’t wait to see these results! #researchinnovation #gaitanalysis
For more, visit novelusa.com/loadsol

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Rafa Aguilar

@raguilar.es

7 months ago

(PDF) What Does Gait Reveal About Health? Investigating Human Motion as an Indicator PDF | Gait analysis offers a non-invasive, scalable approach to infer individual health information using vision-based methods. In this work, we propose... | Find, read and cite all the research you n...

🚶‍♂️💡What can your gait say about your health?

Excited to share our new work at #CAIP2025. We propose a multi-task framework to estimate 12 health traits from just silhouette gait videos — no wearables, no pose.
📄 rb.gy/727g8b
#GaitAnalysis #HealthAI #ComputerVision

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@ecssofficial.bsky.social

8 months ago

☕ I'll be around until Wednesday – happy to connect over coffee, just drop me a DM!

#ESB2025 #ESB #Biomechanics #AIinBiomechanics #ExplainableAI #GaitAnalysis #HumanMovement #ML #XAI

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ECSS

8 months ago

Ever wondered about the benefits of digital force plates using piezoelectric technology?

See Kistler's solution live at booth 46 at #ECSS2025 in Rimini – combining precision & innovation in biomechanics.

#SportsScience #Biomechanics #Lifesciences #ForcePlate #GaitAnalysis #MotionAnalysis #Kistler

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PONI Lab

@ponilabresearch.bsky.social

9 months ago

📝 Bonus: Upon request, our study team will provide a letter confirming your participation, which you can use as proof of volunteer hours.

📍 Interested? email poni.lab@ucalgary.ca to schedule your data collection session today!
#participateinresearch #motioncapture #pediatricresearch #gaitanalysis

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novel - Accurate force & pressure sensors

@novel-artinscience.bsky.social

9 months ago

Get more out of every step! For a limited time, order any #pedar system and receive a free pair of insoles. This offer is valid until June 30. #biomechanics #footwear #diabetes #gaitanalysis #ClinicalResearch #SportScience
novelusa.com/pedar

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10 months ago

This is a new approach to #personalized #rehabilitation based on #ML, unlocking novel opportunities in #injury #prevention and therapy. Our primary goal is to support clinicians and #patients in improving therapeutic outcomes.

#AI #MachineLearning #Rehabilitation #Sonification #GaitAnalysis

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Verena Hackl

@verhac.bsky.social

10 months ago

u.a. RIESEN Bildschirm (während Motion Capture Programm läuft) auf Arbeitstisch in Labor

Na das nenne ich mal einen Arbeitsbildschirm für Motion Capture… 😅🥹😅

#motioncapture #gaitanalysis

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Cassie C.

@cassandra6.bsky.social

10 months ago

OMO. Let’s break it down cleanly, with no softening. ⸻ What can government AI systems actually surmise, based on publicly available data? Assume: • Standard North American user (Instagram, TikTok, Facebook, X, LinkedIn, etc.) • Passive surveillance data (CCTV, doorbell cams, mall cams, etc.) • No warrant required = only public data streams and commercial access ⸻ 1. Personality, Psychology, and Mental Health AI can infer: • Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) • Attachment style (avoidant, anxious, secure) via post tone and relational comments • Depression/anxiety markers (based on language patterns, photo composition, posting frequency, time of night) • Narcissism/sociopathy likelihood (via bragging patterns, lack of empathy cues, exploitation of trends) • Political rage susceptibility or authoritarian followship traits (e.g., who you’re resharing, moral disgust language) How: Language models + facial expression analysis + repost clustering + time/behavioral rhythm. ⸻

Relationship Networks (Real and Hidden) AI can detect: • Close relationships (not just follows but comment tone, likes, photo interactions, time proximity) • Secret relationships or affairs (e.g., travel coincidence with someone you don’t follow but appear next to in photos) • Estrangement (e.g., no longer liking family posts, absence from events you previously attended) • Radicalization chains (who you listen to, how recently, and who you start repeating) How: Graph analysis + proximity mapping + sentiment weight over time. ⸻

⸻ 4. Socioeconomic Class and Financial Pressure AI can infer: • Income band (based on phone model, travel frequency, clothing brands, speech markers) • Financial strain (posts about prices, job frustration, payday behavior, skipped vacations) • Employment vulnerability (LinkedIn profile silence, frequent job changes, resume scraping) How: NLP of complaint frequency + purchase metadata + LinkedIn crawl + commercial dataset overlays. ⸻ 5. Health Status and Likely Diagnoses AI can predict: • Chronic illness (e.g., visible fatigue, swollen joints, food restriction patterns) • Substance use (speech slurring in videos, eye dilation, visible track marks, erratic posting) • Eating disorder risk (pose angles, body-checking, food diary posts, thirst-trap patterns) How: Computer vision + symptom keyword matching + behavior anomalies over time. ⸻ 6. Belief Systems and Propaganda Vulnerability AI can detect: • What religion you follow or are lapsed from • How likely you are to believe conspiracy content (based on who you follow, what you question, phrasing patterns like “they won’t tell us…”) • Cognitive rigidity or flexibility (e.g., do you reframe, joke, argue, deflect?) How: Large language model pattern recognition + memory mapping over ideological shifts.

ChatGPT on AI’s current capacities, based on typical social media use and public cameras #AI #analysis #facialrecognition #surveillance #resist #ChatGPT #protests #facialsurveillance #gaitanalysis

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Assessment of Gait Parameters Using Wearable Sensors and Their Association With Muscle Mass, Strength, and Physical Performance in Korean Older Adults: Cross-Sectional Study Background: Gait speed indicates of onset or decline of physical performance in sarcopenia. However, real-time measurements of other gait parameters, such as step length, stride length, step width, and support time, are limited. The advent of wearable technology has facilitated the measurement of these parameters, necessitating further investigation into their potential applications. Objective: This study aimed to investigate the relationship between gait parameters measured using wearable sensors and muscle mass, strength, and physical performance in community-dwelling older adults. Methods: In a cross-sectional study of 91 participants aged ≥ 65 years, gait parameters, such as step count, step length, cadence, single and double support times, vertical oscillation, and instantaneous vertical loading rate (IVLR), measured by a wireless earbud device, were analyzed with respect to the appendicular skeletal muscle mass index (SMI), calf circumference, hand grip strength, 5-time chair stand test, short physical performance battery (SPPB), and SARC-F questionnaire: strength, assistance with walking, rise from a chair, climb stairs and falls. This study was conducted from July 10, 2023, and November 1, 2023, at an outpatient clinic of a university hospital in Seoul, Korea. Multiple regression analysis was performed to investigate independent associations after adjusting for age, sex, body mass index, and comorbidities. Results: Among 91 participants (45 men and 46 women; mean age: 74.1 for men and 73.6 for women), gait speed and vertical oscillation showed negative associations with the 5-time chair stand test (p < 0.001) and SARC-F, but positive associations with SPPB (p < 0.001). Vertical oscillations were also associated with grip strength (p = 0.003). Single and double support times were associated with the 5-time chair stand test and SPPB (p < 0.001). In addition, double support time was associated with SARC-F scores (p < 0.001). Gait speed, support time, vertical oscillation, and IVLR showed independent associations with the 5-time chair stand test and SPPB (p < 0.001), both related to muscle strength or physical performance. Gait speed, double support time, and vertical oscillation all had significant associations with SARC-F scores. Conclusions: This study demonstrated a significant association between gait monitoring using wearable sensors and quantitative assessments of muscle strength and physical performance in elderly people. Furthermore, this study substantiated the extensive applicability of diverse gait parameters in predicting sarcopenia.

11 months ago

JMIR Formative Res: Assessment of Gait Parameters Using Wearable Sensors and Their Association With Muscle Mass, Strength, and Physical Performance in Korean Older Adults: Cross-Sectional Study #GaitAnalysis #WearableTechnology #SeniorHealth #PhysicalPerformance #Sarcopenia

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Model-Based Feature Extraction and Classification for Parkinson Disease Screening Using Gait Analysis: Development and Validation Study Background: Parkinson disease (PD) is a progressive neurodegenerative disorder that affects motor coordination, leading to gait abnormalities. Early detection of PD is crucial for effective management and treatment. Traditional diagnostic methods often require invasive procedures or are performed when the disease has significantly progressed. Therefore, there is a need for noninvasive techniques that can identify early motor symptoms, particularly those related to gait. Objective: The study aimed to develop a noninvasive approach for the early detection of PD by analyzing model-based gait features. The primary focus is on identifying subtle gait abnormalities associated with PD using kinematic characteristics. Methods: Data were collected through controlled video recordings of participants performing the timed up and go (TUG) assessment, with particular emphasis on the turning phase. The kinematic features analyzed include shoulder distance, step length, stride length, knee and hip angles, leg and arm symmetry, and trunk angles. These features were processed using advanced filtering techniques and analyzed through machine learning methods to distinguish between normal and PD-affected gait patterns. Results: The analysis of kinematic features during the turning phase of the TUG assessment revealed that individuals with PD exhibited subtle gait abnormalities, such as freezing of gait, reduced step length, and asymmetrical movements. The model-based features proved effective in differentiating between normal and PD-affected gait, demonstrating the potential of this approach in early detection. Conclusions: This study presents a promising noninvasive method for the early detection of PD by analyzing specific gait features during the turning phase of the TUG assessment. The findings suggest that this approach could serve as a sensitive and accurate tool for diagnosing and monitoring PD, potentially leading to earlier intervention and improved patient outcomes.

11 months ago

New in JMIR Aging: Model-Based Feature Extraction and Classification for Parkinson Disease Screening Using Gait Analysis: Development and Validation Study #ParkinsonsDisease #GaitAnalysis #NeurodegenerativeDisorders #EarlyDetection #MotorCoordination

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LinkedIn This link will take you to a page that’s not on LinkedIn

11 months ago

🚀 𝗧𝗿𝘆 𝗶𝘁 𝗼𝘂𝘁 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳!
🔗 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗵𝗲𝗿𝗲: github.com/fhstp/Intell...
💻 𝗡𝗼 𝗶𝗻𝘀𝘁𝗮𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗻𝗲𝗲𝗱𝗲𝗱: just 𝗱𝗼𝘄𝗻𝗹𝗼𝗮𝗱, 𝘂𝗻𝗽𝗮𝗰𝗸, and 𝗿𝘂𝗻 in Vicon Nexus 2.14+

💡 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗮𝗵𝗲𝗮𝗱: Stay tuned for an even larger evaluation coming soon!

#Gait #GaitAnalysis #Biomechanics #Kinematics #MachineLearning #DeepLearning

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NUSHU by Magnes

@magnesnushu.bsky.social

11 months ago

Physical Therapists, let’s take patient care to the next level!

Join the NUSHU Partner Program and enhance mobility with real-time gait insights. Improve outcomes, support your patients, and earn from our collaboration.

📩 DM us to learn more!
#PhysicalTherapy #GaitAnalysis #SmartRehab #NUSHU

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