5 research outputs found
Making Sensors, Making Sense, Making Stimuli: The State of the Art in Wearables Research from ISWC 2019
The International Symposium on Wearable Computers (ISWC) has been the leading research venue for wearable technology research since 1997. This year, the 23rd ISWC was held in London, UK from Sept 9-13th. Following on the last 8 years of successful collaboration, ISWC was co-located with the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)
Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
For people with chronic pain, the assessment of protective behavior during
physical functioning is essential to understand their subjective pain-related
experiences (e.g., fear and anxiety toward pain and injury) and how they deal
with such experiences (avoidance or reliance on specific body joints), with the
ultimate goal of guiding intervention. Advances in deep learning (DL) can
enable the development of such intervention. Using the EmoPain MoCap dataset,
we investigate how attention-based DL architectures can be used to improve the
detection of protective behavior by capturing the most informative temporal and
body configurational cues characterizing specific movements and the strategies
used to perform them. We propose an end-to-end deep learning architecture named
BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts
that are more informative to the detection of protective behavior. The approach
addresses the variety of ways people execute a movement (including healthy
people) independently of the type of movement analyzed. Through extensive
comparison experiments with other state-of-the-art machine learning techniques
used with motion capture data, we show statistically significant improvements
achieved by using these attention mechanisms. In addition, the BANet
architecture requires a much lower number of parameters than the state of the
art for comparable if not higher performances.Comment: 7 pages, 3 figures, 2 tables, code available, accepted in ACII 201
UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language
We introduce UbiPhysio, a milestone framework that delivers fine-grained
action description and feedback in natural language to support people's daily
functioning, fitness, and rehabilitation activities. This expert-like
capability assists users in properly executing actions and maintaining
engagement in remote fitness and rehabilitation programs. Specifically, the
proposed UbiPhysio framework comprises a fine-grained action descriptor and a
knowledge retrieval-enhanced feedback module. The action descriptor translates
action data, represented by a set of biomechanical movement features we
designed based on clinical priors, into textual descriptions of action types
and potential movement patterns. Building on physiotherapeutic domain
knowledge, the feedback module provides clear and engaging expert feedback. We
evaluated UbiPhysio's performance through extensive experiments with data from
104 diverse participants, collected in a home-like setting during 25 types of
everyday activities and exercises. We assessed the quality of the language
output under different tuning strategies using standard benchmarks. We
conducted a user study to gather insights from clinical experts and potential
users on our framework. Our initial tests show promise for deploying UbiPhysio
in real-life settings without specialized devices.Comment: 27 pages, 14 figures, 5 table
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data
Protective behavior exhibited by people with chronic pain (CP) during
physical activities is the key to understanding their physical and emotional
states. Existing automatic protective behavior detection (PBD) methods rely on
pre-segmentation of activities predefined by users. However, in real life,
people perform activities casually. Therefore, where those activities present
difficulties for people with chronic pain, technology-enabled support should be
delivered continuously and automatically adapted to activity type and
occurrence of protective behavior. Hence, to facilitate ubiquitous CP
management, it becomes critical to enable accurate PBD over continuous data. In
this paper, we propose to integrate human activity recognition (HAR) with PBD
via a novel hierarchical HAR-PBD architecture comprising graph-convolution and
long short-term memory (GC-LSTM) networks, and alleviate class imbalances using
a class-balanced focal categorical-cross-entropy (CFCC) loss. Through in-depth
evaluation of the approach using a CP patients' dataset, we show that the
leveraging of HAR, GC-LSTM networks, and CFCC loss leads to clear increase in
PBD performance against the baseline (macro F1 score of 0.81 vs. 0.66 and
precision-recall area-under-the-curve (PR-AUC) of 0.60 vs. 0.44). We conclude
by discussing possible use cases of the hierarchical architecture in CP
management and beyond. We also discuss current limitations and ways forward.Comment: Submitted to PACM IMWU
Recurrent Network based Automatic Detection of Chronic Pain Protective Behavior using MoCap and sEMG data
In chronic pain physical rehabilitation, physiotherapists adapt exercise sessions according to the movement behavior of patients. As rehabilitation moves beyond clinical sessions, technology is needed to similarly assess movement behaviors and provide such personalized support. In this paper, as a first step, we investigate automatic detection of protective behavior (movement behavior due to pain-related fear or pain) based on
wearable motion capture and electromyography sensor data. We investigate two recurrent networks (RNN) referred to as stackedLSTM and dual-stream LSTM, which we compare with related deep learning (DL) architectures. We further explore data augmentation techniques and additionally analyze the impact of segmentation window lengths on detection performance. The leading performance of 0.815 mean F1 score achieved by stacked LSTM provides important grounding for the development of wearable technology to support chronic pain physical rehabilitation during daily activities