164 research outputs found
Assessing the State of Self-Supervised Human Activity Recognition using Wearables
The emergence of self-supervised learning in the field of wearables-based
human activity recognition (HAR) has opened up opportunities to tackle the most
pressing challenges in the field, namely to exploit unlabeled data to derive
reliable recognition systems for scenarios where only small amounts of labeled
training samples can be collected. As such, self-supervision, i.e., the
paradigm of 'pretrain-then-finetune' has the potential to become a strong
alternative to the predominant end-to-end training approaches, let alone
hand-crafted features for the classic activity recognition chain. Recently a
number of contributions have been made that introduced self-supervised learning
into the field of HAR, including, Multi-task self-supervision, Masked
Reconstruction, CPC, and SimCLR, to name but a few. With the initial success of
these methods, the time has come for a systematic inventory and analysis of the
potential self-supervised learning has for the field. This paper provides
exactly that. We assess the progress of self-supervised HAR research by
introducing a framework that performs a multi-faceted exploration of model
performance. We organize the framework into three dimensions, each containing
three constituent criteria, such that each dimension captures specific aspects
of performance, including the robustness to differing source and target
conditions, the influence of dataset characteristics, and the feature space
characteristics. We utilize this framework to assess seven state-of-the-art
self-supervised methods for HAR, leading to the formulation of insights into
the properties of these techniques and to establish their value towards
learning representations for diverse scenarios.Comment: update
On the Benefit of Generative Foundation Models for Human Activity Recognition
In human activity recognition (HAR), the limited availability of annotated
data presents a significant challenge. Drawing inspiration from the latest
advancements in generative AI, including Large Language Models (LLMs) and
motion synthesis models, we believe that generative AI can address this data
scarcity by autonomously generating virtual IMU data from text descriptions.
Beyond this, we spotlight several promising research pathways that could
benefit from generative AI for the community, including the generating
benchmark datasets, the development of foundational models specific to HAR, the
exploration of hierarchical structures within HAR, breaking down complex
activities, and applications in health sensing and activity summarization.Comment: Generative AI for Pervasive Computing (GenAI4PC) Symposium within
UbiComp/ISWC 202
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data
Previous work has demonstrated that virtual accelerometry data, extracted
from videos using cross-modality transfer approaches like IMUTube, is
beneficial for training complex and effective human activity recognition (HAR)
models. Systems like IMUTube were originally designed to cover activities that
are based on substantial body (part) movements. Yet, life is complex, and a
range of activities of daily living is based on only rather subtle movements,
which bears the question to what extent systems like IMUTube are of value also
for fine-grained HAR, i.e., When does IMUTube break? In this work we first
introduce a measure to quantitatively assess the subtlety of human movements
that are underlying activities of interest--the motion subtlety index
(MSI)--which captures local pixel movements and pose changes in the vicinity of
target virtual sensor locations, and correlate it to the eventual activity
recognition accuracy. We then perform a "stress-test" on IMUTube and explore
for which activities with underlying subtle movements a cross-modality transfer
approach works, and for which not. As such, the work presented in this paper
allows us to map out the landscape for IMUTube applications in practical
scenarios
Towards Using Unlabeled Data in a Sparse-coding Framework for Human Activity Recognition
We propose a sparse-coding framework for activity recognition in ubiquitous
and mobile computing that alleviates two fundamental problems of current
supervised learning approaches. (i) It automatically derives a compact, sparse
and meaningful feature representation of sensor data that does not rely on
prior expert knowledge and generalizes extremely well across domain boundaries.
(ii) It exploits unlabeled sample data for bootstrapping effective activity
recognizers, i.e., substantially reduces the amount of ground truth annotation
required for model estimation. Such unlabeled data is trivial to obtain, e.g.,
through contemporary smartphones carried by users as they go about their
everyday activities.
Based on the self-taught learning paradigm we automatically derive an
over-complete set of basis vectors from unlabeled data that captures inherent
patterns present within activity data. Through projecting raw sensor data onto
the feature space defined by such over-complete sets of basis vectors effective
feature extraction is pursued. Given these learned feature representations,
classification backends are then trained using small amounts of labeled
training data.
We study the new approach in detail using two datasets which differ in terms
of the recognition tasks and sensor modalities. Primarily we focus on
transportation mode analysis task, a popular task in mobile-phone based
sensing. The sparse-coding framework significantly outperforms the
state-of-the-art in supervised learning approaches. Furthermore, we demonstrate
the great practical potential of the new approach by successfully evaluating
its generalization capabilities across both domain and sensor modalities by
considering the popular Opportunity dataset. Our feature learning approach
outperforms state-of-the-art approaches to analyzing activities in daily
living.Comment: 18 pages, 12 figures, Pervasive and Mobile Computing, 201
The ambient kitchen: a pervasive sensing environment for situated services
In this paper we describe the demonstration of the Ambient Kitchen, a pervasive sensing environment designed for improving cooking skills, promoting healthier eating, and helping cognitively impaired people to live more independent in their own homes. The kitchen is instrumented with an embedded sensing infrastructure including RFID, Newcastle University Culture lab’s proprietary wireless accelerometers (WAX), microphone, camera, pressure sensors and tablet computers. Several applications including real-time activity recognition, recipe displays, and real-time food recognition are deployed in our kitchen
Cardiorespiratory fitness is associated with hard and light intensity physical activity but not time spent sedentary in 10–14 year old schoolchildren: the HAPPY study
Sedentary behaviour is a major risk factor for developing chronic diseases and is associated with low cardiorespiratory fitness in adults. It remains unclear how sedentary behaviour and different physical activity subcomponents are related to cardiorespiratory fitness in children. The purpose of this study was to assess how sedentary behaviour and different physical activity subcomponents are associated with 10–14 year-old schoolchildren's cardiorespiratory fitness
Video Based Assessment of OSATS Using Sequential Motion Textures
Presented at the Fifth Workshop on Modeling and Monitoring of Computer Assisted Interventions (M2CAI)We present a fully automated framework for video based surgical skill assessment that incorporates the sequential and qualitative aspects of surgical motion in a data-driven manner. We replicate Objective Structured Assessment of Technical Skills (OSATS) assessments, which provides both an overall and in-detail evaluation of basic suturing skills required for surgeons. Video analysis techniques are introduced that incorporate sequential motion aspects into motion textures. We also
demonstrate significant performance improvements over standard bag-of-words and motion analysis approaches. We evaluate our framework in a
case study that involved medical students with varying levels of expertise performing basic surgical tasks in a surgical training lab setting.Intuitive Surgica
Occupancy monitoring using environmental & context sensors and a hierarchical analysis framework
Saving energy in residential and commercial buildings is of great interest due to diminishing resources. Heating ventilation and air conditioning systems, and electric lighting are responsible for a significant share of energy usage, which makes it desirable to optimise their operations while maintaining user comfort. Such optimisation requires accurate occupancy estimations. In contrast to current, often invasive or unreliable methods we present an approach for accurate occupancy estimation using a wireless sensor network (WSN) that only collects non-sensitive data and a novel, hierarchical analysis method. We integrate potentially uncertain contextual information to produce occupancy estimates at different levels of granularity and provide confidence measures for effective building management. We evaluate our framework in real-world deployments and demonstrate its effectiveness and accuracy for occupancy monitoring in both low-and high-traffic area scenarios. Furthermore, we show how the system is used for analysing historical data and identify effective room misuse and thus a potential for energy saving
- …