Tootfinder

Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contrastive Dual-Path Learning Approach
Panpan Ji, Junni Song, Hang Xiao, Hanyu Liu, Chao Li
https://arxiv.org/abs/2507.02826

Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contrastive Dual-Path Learning Approach
Sensor-based Human Activity Recognition (HAR) is a core technology that enables intelligent systems to perceive and interact with their environment. However, multimodal HAR systems still encounter key challenges, such as difficulties in cross-modal feature alignment and imbalanced modality contributions. To address these issues, we propose a novel framework called the Dynamic Contrastive Dual-Path Network (DCDP-HAR). The framework comprises three key components. First, a dual-path feature extra…

IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition
Haozhe Zhou, Riku Arakawa, Yuvraj Agarwal, Mayank Goel
https://arxiv.org/abs/2508.01894

IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition
IMUs are regularly used to sense human motion, recognize activities, and estimate full-body pose. Users are typically required to place sensors in predefined locations that are often dictated by common wearable form factors and the machine learning model's training process. Consequently, despite the increasing number of everyday devices equipped with IMUs, the limited adaptability has seriously constrained the user experience to only using a few well-explored device placements (e.g., wrist and …

Robust Scaling in Human Brain Dynamics Despite Latent Variables and Limited Sampling Distortions
Rub\'en Calvo, Carles Martorell, Adri\'an Roig, Miguel A. Mu\~noz
https://arxiv.org/abs/2506.03640

Robust Scaling in Human Brain Dynamics Despite Latent Variables and Limited Sampling Distortions
The idea that information-processing systems operate near criticality to enhance computational performance is supported by scaling signatures in brain activity. However, external signals raise the question of whether this behavior is intrinsic or input-driven. We show that autocorrelated inputs and temporal resolution influence observed scaling exponents in simple neural models. We also demonstrate analytically that under subsampling, non-critical systems driven by independent autocorrelated si…

How urban scaling and resource distribution shape social welfare and migration dynamics
Bryce Morsky
https://arxiv.org/abs/2506.03384 https://

How urban scaling and resource distribution shape social welfare and migration dynamics
Many outputs of cities scale in universal ways, including infrastructure, crime, and economic activity. Through a mathematical model, this study investigates the interplay between such scaling laws in human organization and governmental allocations of resources, focusing on impacts to migration patterns and social welfare. We find that if superlinear scaling resources of cities -- such as economic and social activity -- are the primary drivers of city dwellers' utility, then cities tend to conv…

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network
Ying Yu, Hang Xiao, Siyao Li, Jiarui Li, Haotian Tang, Hanyu Liu, Chao Li
https://arxiv.org/abs/2507.02827

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network
The primary objective of human activity recognition (HAR) is to infer ongoing human actions from sensor data, a task that finds broad applications in health monitoring, safety protection, and sports analysis. Despite proliferating research, HAR still faces key challenges, including the scarcity of labeled samples for rare activities, insufficient extraction of high-level features, and suboptimal model performance on lightweight devices. To address these issues, this paper proposes a comprehensi…

Modeling Human Spatial Mobility Patterns with the L\'evy Flight Cluster Model
Malcolm Wolff, Adrian Dobra, Anton H. Westveld, Grace S. Chiu
https://arxiv.org/abs/2509.00298 …

Modeling Human Spatial Mobility Patterns with the Lévy Flight Cluster Model
Despite the extensive collection of individual mobility data over the past decade, fueled by the widespread use of GPS-enabled personal devices, the existing statistical literature on estimating human spatial mobility patterns from temporally irregular location data remains limited. In this paper, we introduce the Lévy Flight Cluster Model (LFCM), a hierarchical Bayesian mixture model designed to analyze an individual's activity distribution. The LFCM can be utilized to determine probabilistic…

Fedi meta-musings.
Just went to look at a Mastodon account I interacted with a little while back. Their follow requests require approval (which is fair enough) and the bio states
Got a blank or nonsensical avatar, no visible activity, no pointers to your identity? I'll ignore your follow request.
Well I guess that's me out. I have a good reason for wanting to be pseudonymous, as do many others here I imagine.
Of course it's every user's right to set whatever conditions they want on who follows them, but a blanket refusal on anyone not featuring a "real name" and a human-appearing avatar feels quite over-sensitive to me.
#fediverse #mastodon

HARNode: A Time-Synchronised, Open-Source, Multi-Device, Wearable System for Ad Hoc Field Studies
Philipp Lepold, Tobias R\"oddiger, Michael Beigl
https://arxiv.org/abs/2506.03219

HARNode: A Time-Synchronised, Open-Source, Multi-Device, Wearable System for Ad Hoc Field Studies
Research into human activity recognition (HAR) suffers from a lack of comprehensive, freely available field data. Commercial systems are rarely open source, offer little expandability, and have shortcomings in node synchronisation, data throughput, placement clarity, setup complexity, and cost. As a result, only a few intuitively placed sensors are often used, and field trials are generally reduced. HARNode addresses these obstacles as a fully open-source hardware and software platform for rapi…

"We can conclude that the brain has the capacity to distinguish virtual infectious patterns,
become activated,
and link this activation to a downstream response,
⭐️ resulting in systemic immunity,"
wrote Camilla Jandus, co-author of the study paper
https://www.

Human immune system reacts to the mere sight of infection
Researchers monitored brain activity when people were shown images of sick-looking avatars via VR. The brain appeared to spark an immune response.

🎯 Smartwatches allow researchers to identify human activity with 78% accuracy
#smartwatch

Smartwatches allow researchers to identify human activity with 78% accuracy
Researchers have long been able to use information from smartwatches to identify physical movement, such as sitting or walking, that wearers are performing in a controlled lab setting.

The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals
Margarita Loupa, Antonios Argyriou, Yanwei Liu
https://arxiv.org/abs/2507.20657 htt…

The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals
A subset of Human Activity Classification (HAC) systems are based on AI algorithms that use passively collected wireless signals. This paper presents the micro-Doppler attack targeting HAC from wireless orthogonal frequency division multiplexing (OFDM) signals. The attack is executed by inserting artificial variations in a transmitted OFDM waveform to alter its micro-Doppler signature when it reflects off a human target. We investigate two variants of our scheme that manipulate the waveform at …

RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online
Dong Yoon Lee, Alyssa Weakley, Hui Wei, Blake Brown, Keyana Carrion, Shijia Pan
https://arxiv.org/abs/2508.21167

RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online
One in four people dementia live alone, leading family members to take on caregiving roles from a distance. Many researchers have developed remote monitoring solutions to lessen caregiving needs; however, limitations remain including privacy preserving solutions, activity recognition, and model generalizability to new users and environments. Structural vibration sensor systems are unobtrusive solutions that have been proven to accurately monitor human information, such as identification and act…

PHASE: Passive Human Activity Simulation Evaluation
Steven Lamp, Jason D. Hiser, Anh Nguyen-Tuong, Jack W. Davidson
https://arxiv.org/abs/2507.13505 https:…

PHASE: Passive Human Activity Simulation Evaluation
Cybersecurity simulation environments, such as cyber ranges, honeypots, and sandboxes, require realistic human behavior to be effective, yet no quantitative method exists to assess the behavioral fidelity of synthetic user personas. This paper presents PHASE (Passive Human Activity Simulation Evaluation), a machine learning framework that analyzes Zeek connection logs and distinguishes human from non-human activity with over 90\% accuracy. PHASE operates entirely passively, relying on standard …

Automated Classification of Volcanic Earthquakes Using Transformer Encoders: Insights into Data Quality and Model Interpretability
Y. Suzuki, Y. Yukutake, T. Ohminato, M. Yamasaki, Ahyi Kim
https://arxiv.org/abs/2507.01260

Automated Classification of Volcanic Earthquakes Using Transformer Encoders: Insights into Data Quality and Model Interpretability
Precisely classifying earthquake types is crucial for elucidating the relationship between volcanic earthquakes and volcanic activity. However, traditional methods rely on subjective human judgment, which requires considerable time and effort. To address this issue, we developed a deep learning model using a transformer encoder for a more objective and efficient classification. Tested on Mount Asama's diverse seismic activity, our model achieved high F1 scores (0.930 for volcano tectonic, 0.931…

Autonomy by Design: Preserving Human Autonomy in AI Decision-Support
Stefan Buijsman, Sarah Carter, Juan Pablo Berm\'udez
https://arxiv.org/abs/2506.23952

Autonomy by Design: Preserving Human Autonomy in AI Decision-Support
AI systems increasingly support human decision-making across domains of professional, skill-based, and personal activity. While previous work has examined how AI might affect human autonomy globally, the effects of AI on domain-specific autonomy -- the capacity for self-governed action within defined realms of skill or expertise -- remain understudied. We analyze how AI decision-support systems affect two key components of domain-specific autonomy: skilled competence (the ability to make inform…

Alignment between Brains and AI: Evidence for Convergent Evolution across Modalities, Scales and Training Trajectories
Guobin Shen, Dongcheng Zhao, Yiting Dong, Qian Zhang, Yi Zeng
https://arxiv.org/abs/2507.01966

Alignment between Brains and AI: Evidence for Convergent Evolution across Modalities, Scales and Training Trajectories
Artificial and biological systems may evolve similar computational solutions despite fundamental differences in architecture and learning mechanisms -- a form of convergent evolution. We demonstrate this phenomenon through large-scale analysis of alignment between human brain activity and internal representations of over 600 AI models spanning language and vision domains, from 1.33M to 72B parameters. Analyzing 60 million alignment measurements reveals that higher-performing models spontaneousl…

Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware
Farah Baracat, Giacomo Indiveri, Elisa Donati
https://arxiv.org/abs/2507.23474 https://

Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware
Accurate finger force estimation is critical for next-generation human-machine interfaces. Traditional electromyography (EMG)-based decoding methods using deep learning require large datasets and high computational resources, limiting their use in real-time, embedded systems. Here, we propose a novel approach that performs finger force regression using spike trains from individual motor neurons, extracted from high-density EMG. These biologically grounded signals drive a spiking neural network …

Bridging Generalization and Personalization in Wearable Human Activity Recognition via On-Device Few-Shot Learning
Pixi Kang, Julian Moosmann, Mengxi Liu, Bo Zhou, Michele Magno, Paul Lukowicz, Sizhen Bian
https://arxiv.org/abs/2508.15413

Bridging Generalization and Personalization in Wearable Human Activity Recognition via On-Device Few-Shot Learning
Human Activity Recognition (HAR) using wearable devices has advanced significantly in recent years, yet its generalization remains limited when models are deployed to new users. This degradation in performance is primarily due to user-induced concept drift (UICD), highlighting the importance of efficient personalization. In this paper, we present a hybrid framework that first generalizes across users and then rapidly adapts to individual users using few-shot learning directly on-device. By upda…

Distinct weak asymmetric interactions shape human brain functions as probability fluxes
Yoshiaki Horiike, Shin Fujishiro
https://arxiv.org/abs/2508.20961 https://

Distinct weak asymmetric interactions shape human brain functions as probability fluxes
The functional computation of the human brain arises from the collective behaviour of the underlying neural network. The emerging technology enables the recording of population activity in neurons, and the theory of neural networks is expected to explain and extract functional computations from the data. Thermodynamically, a large proportion of the whole-body energy is consumed by the brain, and functional computation of the human brain seems to involve high energy consumption. The human brain,…

Enhancing Fitness Movement Recognition with Attention Mechanism and Pre-Trained Feature Extractors
Shanjid Hasan Nishat, Srabonti Deb, Mohiuddin Ahmed
https://arxiv.org/abs/2509.02511

Enhancing Fitness Movement Recognition with Attention Mechanism and Pre-Trained Feature Extractors
Fitness movement recognition, a focused subdomain of human activity recognition (HAR), plays a vital role in health monitoring, rehabilitation, and personalized fitness training by enabling automated exercise classification from video data. However, many existing deep learning approaches rely on computationally intensive 3D models, limiting their feasibility in real-time or resource-constrained settings. In this paper, we present a lightweight and effective framework that integrates pre-trained…

Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
Wenqi Zheng, Yutaka Arakawa
https://arxiv.org/abs/2508.10371 https://

Few-shot Vision-based Human Activity Recognition with MLLM-based Visual Reinforcement Learning
Reinforcement learning in large reasoning models enables learning from feedback on their outputs, making it particularly valuable in scenarios where fine-tuning data is limited. However, its application in multi-modal human activity recognition (HAR) domains remains largely underexplored. Our work extends reinforcement learning to the human activity recognition domain with multimodal large language models. By incorporating visual reinforcement learning in the training process, the model's gener…

A new wave of climate research is sounding a stark warning:
Human activity may be driving drought more intensely
– and more directly
– than previously understood.
The southwestern United States has been in a historic #megadrought for much of the past two decades,
with its reservoirs including lakes Mead and Powell dipping to record lows
and legal disputes erupting ov…

Climate models reveal how human activity may be locking the Southwest into permanent drought
The drought has been linked to the Pacific Decadal Oscillation, a natural climate pattern. A new study finds global warming is now influencing that natural phenomenon.

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin, Wei-Ting Chen, Kuo-Chung Peng, Prayag Tiwari, Samuel Yen-Chi Chen, En-Jui Kuo
https://arxiv.org/abs/2508.06078

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into federated learning.Within Fed-QK-LSTM framework, we enhance human activity recognition (HAR) in privacy-sensitive environments and leverage quantum computing for distributed learning systems.The DeepConv-QK-LSTM architecture on each client node employs convolutional layers for efficient local pattern capture, this de…

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Weiyu Luo, Chenfeng Xiong
https://arxiv.org/abs/2508.02734 …

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Location-Based Service (LBS) data provides critical insights into human mobility, yet its sparsity often yields incomplete trip and activity sequences, making accurate inferences about trips and activities difficult. We raise a research problem: Can we use activity sequences derived from high-quality LBS data to recover incomplete activity sequences at the individual level? This study proposes a new solution, the Variable Selection Network-fused Insertion Transformer (VSNIT), integrating the In…

Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher
Tomas Gallo Aquino, Victoria Liu, Habiba Azab, Raissa Mathura, Andrew J Watrous, Eleonora Bartoli, Benjamin Y Hayden, Paul Sajda, Sameer A Sheth, Nuttida Rungratsameetaweemana
https://arxiv.org/abs/2506.20834

Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher
Transfer learning enhances the training of novel sensory and decision models by employing rich feature representations from large, pre-trained teacher models. Cognitive neuroscience shows that the human brain creates low-dimensional, abstract representations for efficient sensorimotor coding. Importantly, the brain can learn these representations with significantly fewer data points and less computational power than artificial models require. We introduce Brain2Model Transfer Learning (B2M), a …

Real-time Generation of Various Types of Nodding for Avatar Attentive Listening System
Kazushi Kato, Koji Inoue, Divesh Lala, Keiko Ochi, Tatsuya Kawahara
https://arxiv.org/abs/2507.23298

Real-time Generation of Various Types of Nodding for Avatar Attentive Listening System
In human dialogue, nonverbal information such as nodding and facial expressions is as crucial as verbal information, and spoken dialogue systems are also expected to express such nonverbal behaviors. We focus on nodding, which is critical in an attentive listening system, and propose a model that predicts both its timing and type in real time. The proposed model builds on the voice activity projection (VAP) model, which predicts voice activity from both listener and speaker audio. We extend it …

All the bird types who come and feed in my small bird sanctuary (aka garden) have fledglings right now. Many of them bring the little ones along to feed. This is a great opportunity for me to learn what the various birds look like at different ages.
I can't do much about the mess the human world is in; seeing to it that there is a safe space for these young birds to splash in a bird bath and eat good, tasty food is a better activity than just screaming into the void.

Deep Learning for Skeleton Based Human Motion Rehabilitation Assessment: A Benchmark
Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
https://arxiv.org/abs/2507.21018

Deep Learning for Skeleton Based Human Motion Rehabilitation Assessment: A Benchmark
Automated assessment of human motion plays a vital role in rehabilitation, enabling objective evaluation of patient performance and progress. Unlike general human activity recognition, rehabilitation motion assessment focuses on analyzing the quality of movement within the same action class, requiring the detection of subtle deviations from ideal motion. Recent advances in deep learning and video-based skeleton extraction have opened new possibilities for accessible, scalable motion assessment …

Multinomial thresholded LASSO for interpretable dimension reduction of human activity sequences
Zuofu Huang, Yingling Fan, James Hodges, Julian Wolfson
https://arxiv.org/abs/2507.17900

Multinomial thresholded LASSO for interpretable dimension reduction of human activity sequences
The widespread collection of data from mobile and wearable devices has created unprecedented opportunities to study human behavior in fine temporal resolution. One common structure for such data is categorical sequences: ordered, multinomial observations across many time points. These sequences present unique statistical challenges due to their high dimensionality and complex temporal dependence, including both short- and long-term correlations. Yet, there has been relatively little methodologi…

Scaling and Distilling Transformer Models for sEMG
Nicholas Mehlman, Jean-Christophe Gagnon-Audet, Michael Shvartsman, Kelvin Niu, Alexander H. Miller, Shagun Sodhani
https://arxiv.org/abs/2507.22094

Scaling and Distilling Transformer Models for sEMG
Surface electromyography (sEMG) signals offer a promising avenue for developing innovative human-computer interfaces by providing insights into muscular activity. However, the limited volume of training data and computational constraints during deployment have restricted the investigation of scaling up the model size for solving sEMG tasks. In this paper, we demonstrate that vanilla transformer models can be effectively scaled up on sEMG data and yield improved cross-user performance up to 110M…

Removing motion artifacts from mechanomyographic signals: an innovative filtering method applied to human movement analysis
Matthieu Correa (CIAMS), Nicolas Vignais (UR2, M2S, ComBO), Isabelle A. Siegler (CIAMS), Maxime Projetti
https://arxiv.org/abs/2508.20602

Removing motion artifacts from mechanomyographic signals: an innovative filtering method applied to human movement analysis
Mechanomyography (MMG) is a promising tool for measuring muscle activity in the field but its sensitivity to motion artifacts limits its application. In this study, we proposed an adaptative filtering method for MMG accelerometers based on the complete ensemble empirical mode decomposition, with adaptative noise and spectral fuzzy entropy, to isolate motions artefacts from the MMG signal in dynamic conditions. We compared our method with the traditional band-pass filtering technique, demonstrat…

Large Model Driven Solar Activity AI Forecaster: A Scalable Dual Data-Model Framework
Jingjing Wang, Pengyu Liang, Tingyu Wang, Ming Li, Yanmei Cui, Siwei Liu, Xin Huang, Xiang Li, Minghui Zhang, Yunshi Zeng, Zhu Cao, Jiekang Feng, Qinghua Hu, Bingxian Luo, Bing Cao
https://arxiv.org/abs/2508.06892…

Large Model Driven Solar Activity AI Forecaster: A Scalable Dual Data-Model Framework
Solar activity drives space weather, affecting Earth's magnetosphere and technological infrastructure, which makes accurate solar flare forecasting critical. Current space weather models under-utilize multi-modal solar data, lack iterative enhancement via expert knowledge, and rely heavily on human forecasters under the Observation-Orientation-Decision-Action (OODA) paradigm. Here we present the "Solar Activity AI Forecaster", a scalable dual data-model driven framework built on foundational mo…

NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery https://arxiv.org/abs/2506.06898

NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery
We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI activity paired with seen images that enabled unprecedented improvements in fMRI-to-image reconstruction efforts. Recent models trained on NSD have been evaluated only on seen image reconstruction. Using NSD-Imagery, it is possible to assess how well these models perform on mental image reconstruction. This is a chal…

WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
Maximilian Burzer, Tobias King, Till Riedel, Michael Beigl, Tobias R\"oddiger
https://arxiv.org/abs/2508.16604

WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
The lack of standardization across Wearable Human Activity Recognition (WHAR) datasets limits reproducibility, comparability, and research efficiency. We introduce WHAR datasets, an open-source library designed to simplify WHAR data handling through a standardized data format and a configuration-driven design, enabling reproducible and computationally efficient workflows with minimal manual intervention. The library currently supports 9 widely-used datasets, integrates with PyTorch and TensorFl…

It's good to see The Economist taking the impact of climate change on the #AMOC seriously. It isn't a particularly good article - no info on likely timescales, no sources - but it is something that should worry us all. Here's Carbon Brief's recent piece:

Ocean current ‘collapse’ could trigger ‘profound cooling’ in northern Europe – even with global warming - Carbon Brief
A “collapse” of key Atlantic ocean currents would cause winter temperatures to plunge across northern Europe, overriding the warming driven by human activity.

Avoid the word "pedestrian." You marginalize a basic human activity when you give it a four-syllable Greek-derived name.
Say walk, on foot, foot traffic whenever you can.

Multi-perspective monitoring of wildlife and human activities from camera traps and drones with deep learning models
Hao Chen, Fang Qiu, Li An, Douglas Stow, Eve Bohnett, Haitao Lyu, Shuang Tian
https://arxiv.org/abs/2508.15629

Multi-perspective monitoring of wildlife and human activities from camera traps and drones with deep learning models
Wildlife and human activities are key components of landscape systems. Understanding their spatial distribution is essential for evaluating human wildlife interactions and informing effective conservation planning. Multiperspective monitoring of wildlife and human activities by combining camera traps and drone imagery. Capturing the spatial patterns of their distributions, which allows the identification of the overlap of their activity zones and the assessment of the degree of human wildlife c…

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Sarah Pungitore, Shashank Yadav, Molly Douglas, Jarrod Mosier, Vignesh Subbian
https://arxiv.org/abs/2506.16359

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Objective: Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review, limited automation, and difficulties in adapting algorithms across sources. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To suppor…

Exploring Economic Sectoral Dynamics Through High-resolution Mobility Data
Timothy F Leslie, Hossein Amiri, Andreas Z\"ufle
https://arxiv.org/abs/2506.13985

Exploring Economic Sectoral Dynamics Through High-resolution Mobility Data
We present a comprehensive dataset capturing patterns of human mobility across the United States from January 2019 to January 2023, based on anonymized mobile device data. Aggregated weekly, the dataset reports visits, travel distances, and time spent at public locations organized by economic sector for approximately 12 million Points of Interest (POIs). This resource enables the study of how mobility and economic activity changed over time, particularly during major events such as the COVID-19…

KAN-HAR: A Human activity recognition based on Kolmogorov-Arnold Network
Mohammad Alikhani
https://arxiv.org/abs/2508.11186 https://arxiv.org/pdf/2508.1118…

KAN-HAR: A Human activity recognition based on Kolmogorov-Arnold Network
Human Activity Recognition (HAR) plays a critical role in numerous applications, including healthcare monitoring, fitness tracking, and smart environments. Traditional deep learning (DL) approaches, while effective, often require extensive parameter tuning and may lack interpretability. In this work, we investigate the use of a single three-axis accelerometer and the Kolmogorov--Arnold Network (KAN) for HAR tasks, leveraging its ability to model complex nonlinear relationships with improved int…

My Personal Privacy - Your resource for achieving better online privacy
Why privacy:
The right to privacy is a well-established, fundamental human right. It is necessary for a safe and just society. It is especially important in regions where intolerance and discrimination are backed by autocratic regimes. While you may not feel the need for privacy in all of your online activity, you should be able to achieve a high level of privacy when you need it. This website and the document…

Multiscale phase oscillations induced by cluster synchronisation in human connectome core network
Bosiljka Tadic, Marija Mitrovic Dankulov, Roderick Melnik
https://arxiv.org/abs/2507.07583

Multiscale phase oscillations induced by cluster synchronisation in human connectome core network
Brain imaging data mapping onto human connectome networks enables the investigation of global brain dynamics, where the brain hubs play an essential role in transferring activity between different brain parts. At this scale, the synchronisation processes are increasingly investigated as one of the key mechanisms revealing many aspects of brain functional coherence in healthy brains and revealing deviations due to various brain disorders. For the human connectome core network, consisting of the …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/5]:
- Bridging Generalization and Personalization in Wearable Human Activity Recognition via On-Device ...
Pixi Kang, Julian Moosmann, Mengxi Liu, Bo Zhou, Michele Magno, Paul Lukowicz, Sizhen Bian

BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends, Mengjia Xu
https://arxiv.org/abs/2508.07106

BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Functional Magnetic Resonance Imaging (fMRI) is an imaging technique widely used to study human brain activity. fMRI signals in areas across the brain transiently synchronise and desynchronise their activity in a highly structured manner, even when an individual is at rest. These functional connectivity dynamics may be related to behaviour and neuropsychiatric disease. To model these dynamics, temporal brain connectivity representations are essential, as they reflect evolving interactions betwe…

MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition
Navid Hasanzadeh, Shahrokh Valaee
https://arxiv.org/abs/2506.12997

MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition
The newly established IEEE 802.11bf Task Group aims to amend the WLAN standard to support advanced sensing applications such as human activity recognition (HAR). Although studies have demonstrated the potential of sub-7 GHz Wi-Fi Channel State Information (CSI) for HAR, no method currently performs reliably in real-world scenarios. This work tackles the poor generalization of Wi-Fi-based HAR by introducing an innovative approach to extracting and utilizing movement-related representations, whic…

Transcranial focused ultrasound for identifying the neural substrate of conscious perception https://arxiv.org/abs/2507.08517 Indeed I see long-term potential for phased-array tFUS as a visual prosthesis for the late-blind. The company Nudge is developing a phased-array tFUS helmet.

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Identifying what aspects of brain activity are responsible for conscious perception remains one of the most challenging problems in science. While progress has been made through psychophysical studies employing EEG and fMRI, research would greatly benefit from improved methods for stimulating the brain in healthy human subjects. Traditional techniques for neural stimulation through the skull, including electrical or magnetic stimulation, suffer from coarse spatial resolution and have limited ab…

SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models
Lala Shakti Swarup Ray, Mengxi Liu, Deepika Gurung, Bo Zhou, Sungho Suh, Paul Lukowicz
https://arxiv.org/abs/2507.06405

SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models
Human Activity Recognition (HAR) with wearable sensors is essential for applications in healthcare, fitness, and human-computer interaction. Bio-impedance sensing offers unique advantages for fine-grained motion capture but remains underutilized due to the scarcity of labeled data. We introduce SImpHAR, a novel framework addressing this limitation through two core contributions. First, we propose a simulation pipeline that generates realistic bio-impedance signals from 3D human meshes using sho…

HabSim: Architecture for modelling disruptions, propagation, detection and repair in deep space habitats
Luca Vaccino, Alana K. Lund, Shirley J. Dyke, Mohsen Azimi, Ethan Vallerga
https://arxiv.org/abs/2506.08903

HabSim: Architecture for modelling disruptions, propagation, detection and repair in deep space habitats
Establishing long-term human settlements in deep space presents significant challenges. Harsh environmental conditions, such as extreme temperature fluctuations, micrometeorite impacts, seismic activity, and exposure to solar and cosmic radiation pose obstacles to the design and operation of habitat systems. Prolonged mission duration and the vast distances from Earth introduce further complications in the form of delayed communication and limited resources, making autonomy especially desirable…

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Physically Plausible Data Augmentations for Wearable IMU-based Human Activity Recognition Using P...
Nobuyuki Oishi, Philip Birch, Daniel Roggen, Paula Lago

DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi
Navid Hasanzadeh, Shahrokh Valaee
https://arxiv.org/abs/2507.12132 https://…

DoRF: Doppler Radiance Fields for Robust Human Activity Recognition Using Wi-Fi
Wi-Fi Channel State Information (CSI) has gained increasing interest for remote sensing applications. Recent studies show that Doppler velocity projections extracted from CSI can enable human activity recognition (HAR) that is robust to environmental changes and generalizes to new users. However, despite these advances, generalizability still remains insufficient for practical deployment. Inspired by neural radiance fields (NeRF), which learn a volumetric representation of a 3D scene from 2D im…

UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Weiheng Lu, Chunfeng Song, Jiamin Wu, Pengyu Zhu, Yuchen Zhou, Weijian Mai, Qihao Zheng, Wanli Ouyang
https://arxiv.org/abs/2506.18962

UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Decoding human brain activity from electroencephalography (EEG) signals is a central challenge at the intersection of neuroscience and artificial intelligence, enabling diverse applications in mental state assessment, clinical monitoring, and human-machine interaction. Recent efforts have extensively explored EEG-based brain foundation models for generalized brain decoding, employing large-scale training on multiple datasets. However, most of these attempts struggle with generalizability and fa…

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Sizhen Bian, Mengxi Liu, Vitor Fortes Rey, Daniel Geissler, Paul Lukowicz
https://arxiv.org/abs/2507.07949

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces TinierHAR, an ultra-lightweight deep learning architecture that synergizes residual depthwise separable convolutions, gated recurrent units (GRUs), and temporal aggregation to achieve SOTA efficiency without compromising performance. Evaluated across 14 public HAR datasets, TinierHAR reduces Parameters by 2.7x (vs. TinyHA…

Decoding Polyphenol-Protein Interactions with Deep Learning: From Molecular Mechanisms to Food Applications
Qiang Liu, Tiantian Wang, Binbin Nian, Feiyang Ma, Siqi Zhao, Andr\'es F. V\'asquez, Liping Guo, Chao Ding, Mehdi D. Davari
https://arxiv.org/abs/2508.03456

Decoding Polyphenol-Protein Interactions with Deep Learning: From Molecular Mechanisms to Food Applications
Polyphenols and proteins are essential biomolecules that influence food functionality and, by extension, human health. Their interactions -- hereafter referred to as PhPIs (polyphenol-protein interactions) -- affect key processes such as nutrient bioavailability, antioxidant activity, and therapeutic efficacy. However, these interactions remain challenging due to the structural diversity of polyphenols and the dynamic nature of protein binding. Traditional experimental techniques like nuclear m…

A Linear Generative Framework for Structure-Function Coupling in the Human Brain
Sam Frank Kelemen, Joaqu\'in G\~oni, S\'ergio Pequito, Arian Ashourvan
https://arxiv.org/abs/2507.06136

A Linear Generative Framework for Structure-Function Coupling in the Human Brain
Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC) -- the network of white matter pathways - provides the physical substrate for functional connectivity (FC) -- the correlated neural activity between brain areas. While these structural and functional networks exhibit substantial overlap, their relationship involves complex, indirect mechanisms, including the dynamic interplay of direct and indirect pathways, recurrent netwo…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contra...
Panpan Ji, Junni Song, Yifan Lu, Hang Xiao, Hanyu Liu, Chao Li

"The Amazon Rainforest Approaches a Point of No Return"
#AmazonRainforest #Trees #Nature #Environment

The Amazon Rainforest Approaches a Point of No Return
In “Amazon Tipping Point” — Third-Place Winner of the 2025 Yale Environment 360 Film Contest — Brazilian filmmakers capture striking images of clear-cutting and explore how human activity is so damaging the world’s largest rainforest that it will not be able to recover.

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
Kartik Pandey, Arun Balasubramanian, Debasis Samanta

Insights from the Algonauts 2025 Winners
Paul S. Scotti, Mihir Tripathy
https://arxiv.org/abs/2508.10784 https://arxiv.org/pdf/2508.10784

Insights from the Algonauts 2025 Winners
The Algonauts 2025 Challenge just wrapped up a few weeks ago. It is a biennial challenge in computational neuroscience in which teams attempt to build models that predict human brain activity from carefully curated stimuli. Previous editions (2019, 2021, 2023) focused on still images and short videos; the 2025 edition, which concluded last month (late July), pushed the field further by using long, multimodal movies. Teams were tasked with predicting fMRI responses across 1,000 whole-brain parce…

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz
https://arxiv.org/abs/2507.05532 …

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Inertial measurement units (IMUs) are central to wearable systems for activity recognition and pose estimation, but sensor placement remains largely guided by heuristics and convention. In this work, we introduce Where to Wear (W2W), a simulation-based framework for systematic exploration of IMU placement utility across the body. Using labeled motion capture data, W2W generates realistic synthetic IMU signals at 512 anatomically distributed surface patches, enabling high-resolution, task-specif…

"David Attenborough’s ‘Ocean’ is a brutal, beautiful wake-up call from the sea"
#Oceans #Environment #Film #Media

David Attenborough's 'Ocean' exposes the unseen destruction of the seas
The new film “Ocean With David Attenborough” is both a breathtaking celebration of marine life and a sobering exposé of the industrial forces pushing the ocean toward collapse. With never-before-seen footage from mass coral bleaching to the violent destruction of bottom trawling, it reveals the hidden toll of human activity beneath the waves. But it’s also a story of hope that spotlights thriving marine reserves and the power of protection. Timed to World Oceans Day and the U.N. Ocean Co…

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Daniel K. Freeman, Brian Odegaard, Seung-Schik Yoo, Matthias Michel
https://arxiv.org/abs/2507.08517

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Identifying what aspects of brain activity are responsible for conscious perception remains one of the most challenging problems in science. While progress has been made through psychophysical studies employing EEG and fMRI, research would greatly benefit from improved methods for stimulating the brain in healthy human subjects. Traditional techniques for neural stimulation through the skull, including electrical or magnetic stimulation, suffer from coarse spatial resolution and have limited ab…

Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia
Yifan Wang, Jingyuan Sun, Jichen Zheng, Yunhao Zhang, Chunyu Ye, Jixing Li, Chengqing Zong, Shaonan Wang
https://arxiv.org/abs/2508.04749

Bridging Brains and Models: MoE-Based Functional Lesions for Simulating and Rehabilitating Aphasia
The striking alignment between large language models (LLMs) and human brain activity positions them as powerful models of healthy cognition. This parallel raises a fundamental question: if LLMs can model the intact brain, can we lesion them to simulate the linguistic deficits of the injured brain? In this work, we introduce a methodology to model aphasia - a complex language disorder caused by neural injury - by selectively disabling components in a modular Mixture-of-Experts (MoE) language mod…