3,795 research outputs found
Supporting Everyday Function in Chronic Pain Using Wearable Technology
While most rehabilitation technologies target situated
exercise sessions and associated performance metrics,
physiotherapists recommend physical activities that are
integrated with everyday functioning. We conducted a 1-2
week home study to explore how people with chronic pain
use wearable technology that senses and sonifies movement
(i.e., movement mapped to sound in real-time) to do
functional activity (e.g., loading the dishwasher). Our results
show that real-time movement sonification led to an
increased sense of control during challenging everyday tasks.
Sonification calibrated to functional activity facilitated
application of pain management techniques such as pacing.
When calibrated to individual psychological needs,
sonification enabled serendipitous discovery of physical
capabilities otherwise obscured by a focus on pain or a
dysfunctional proprioceptive system. A physiotherapist was
invited to comment on the implications of our findings. We
conclude by discussing opportunities provided by wearable
sensing technology to enable better functioning, the ultimate
goal of physical rehabilitation
Chronic-Pain Protective Behavior Detection with Deep Learning
In chronic pain rehabilitation, physiotherapists adapt physical activity to
patients' performance based on their expression of protective behavior,
gradually exposing them to feared but harmless and essential everyday
activities. As rehabilitation moves outside the clinic, technology should
automatically detect such behavior to provide similar support. Previous works
have shown the feasibility of automatic protective behavior detection (PBD)
within a specific activity. In this paper, we investigate the use of deep
learning for PBD across activity types, using wearable motion capture and
surface electromyography data collected from healthy participants and people
with chronic pain. We approach the problem by continuously detecting protective
behavior within an activity rather than estimating its overall presence. The
best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross
validation. When protective behavior is modelled per activity type, performance
is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for
sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This
performance reaches excellent level of agreement with the average experts'
rating performance suggesting potential for personalized chronic pain
management at home. We analyze various parameters characterizing our approach
to understand how the results could generalize to other PBD datasets and
different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on
Computing for Healthcar
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
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
Go-with-the-flow: Tracking, Analysis and Sonification of Movement and Breathing to Build Confidence in Activity Despite Chronic Pain
Chronic (persistent) pain (CP) affects one in ten adults; clinical resources are insufficient, and anxiety about activity restricts lives. Technological aids monitor activity but lack necessary psychological support. This paper proposes a new sonification framework, Go-with-the-Flow, informed by physiotherapists and people with CP. The framework proposes articulation of user-defined sonified exercise spaces (SESs) tailored to psychological needs and physical capabilities that enhance body and movement awareness to rebuild confidence in physical activity. A smartphone-based wearable device and a Kinect-based device were designed based on the framework to track movement and breathing and sonify them during physical activity. In control studies conducted to evaluate the sonification strategies, people with CP reported increased performance, motivation, awareness of movement and relaxation with sound feedback. Home studies, a focus group and a survey of CP patients conducted at the end of a hospital pain management session provided an in-depth understanding of how different aspects of the SESs and their calibration can facilitate self-directed rehabilitation and how the wearable version of the device can facilitate transfer of gains from exercise to feared or demanding activities in real life. We conclude by discussing the implications of our findings on the design of technology for physical rehabilitation
Go-with-the-Flow: Tracking, Analysis and Sonification of Movement and Breathing to Build Confidence in Activity Despite Chronic Pain
Chronic (persistent) pain (CP) affects 1 in 10 adults; clinical resources are insufficient, and anxiety about activity restricts lives. Technological aids monitor activity but lack necessary psychological support. This article proposes a new sonification framework, Go-with-the-Flow, informed by physiotherapists and people with CP. The framework proposes articulation of user-defined sonified exercise spaces (SESs) tailored to psychological needs and physical capabilities that enhance body and movement awareness to rebuild confidence in physical activity. A smartphone-based wearable device and a Kinect-based device were designed based on the framework to track movement and breathing and sonify them during physical activity. In control studies conducted to evaluate the sonification strategies, people with CP reported increased performance, motivation, awareness of movement, and relaxation with sound feedback. Home studies, a focus group, and a survey of CP patients conducted at the end of a hospital pain management session provided an in-depth understanding of how different aspects of the SESs and their calibration can facilitate self-directed rehabilitation and how the wearable version of the device can facilitate transfer of gains from exercise to feared or demanding activities in real life. We conclude by discussing the implications of our findings on the design of technology for physical rehabilitation
Roles for Personal Informatics in Chronic Pain
Self-management of chronic pain is a complex and demanding activity. Multidisciplinary pain management programs are designed to provide patients with the skills to improve, maintain functioning and self-manage their pain but gains diminish in the long-term due to lack of support from clinicians. Sensing technology can be a cost-effective way to extend support for self-management outside clinical settings but they are currently under-explored. In this paper, we report studies carried out to investigate how Personal Informatics Systems (PIS) based on wearable body sensing technology could facilitate pain self-management and functioning. Five roles for PIS emerged from a qualitative study with people with chronic pain and physiotherapists: (i) assessment, planning and prevention (ii) a direct supervisory and co-management role, (iii) facilitating deeper understanding, (iv) managing emotional states, and (v) sharing for social acceptability. A web-based survey was conducted to understand the parameters that should be tracked to support self-management and what tracked information should be shared with others. Finally, we suggest an extension to previous PIS models and propose design implications to address immediate, short-term and long-term information needs for personal use of people with chronic pain and for sharing with others. / Note: As originally published there is an error in the document. The following information was omitted by the authors: "The project was funded by the EPSRC grant Emotion & Pain Project EP/H017178/1 rather than the EPSRC grant EP/G043507/1: Pain rehabilitation: E/Motion-based automated coaching.." The article PDF remains unchanged
Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: from Exercise to Functional Activity
Clinicians tailor intervention in chronic pain rehabilitation to movement related self-efficacy (MRSE). This motivates us to investigate automatic MRSE estimation in this context towards the development of technology that is able to provide appropriate support in the absence of a clinician. We first explored clinical observer estimation, which showed that body movement behaviours, rather than facial expressions or engagement behaviours, were more pertinent to MRSE estimation during physical activity instances. Based on our findings, we built a system that estimates MRSE from bodily expressions and bodily muscle activity captured using wearable sensors. Our results (F1 scores of 0.95 and 0.78 in two physical exercise types) provide evidence of the feasibility of automatic MRSE estimation to support chronic pain physical rehabilitation. We further explored automatic estimation of MRSE with a reduced set of low-cost sensors to investigate the possibility of embedding such capabilities in ubiquitous wearable devices to support functional activity. Our evaluation for both exercise and functional activity resulted in F1 score of 0.79. This result suggests the possibility of (and calls for more studies on) MRSE estimation during everyday functioning in ubiquitous settings. We provide a discussion of the implication of our findings for relevant areas
Effects of obesity on walking patterns and adaptability during obstacle crossing
Obesity is a worldwide public health epidemic with no sign of yet abating. Although previous studies have examined the impact of obesity on walking, little is known about the effects of practice on walking patterns in individuals with obesity. The purpose of this current study was to evaluate whether an obstacle-crossing task may detect walking deficits in a group of adults electing to undergo bariatric surgery. With a cross-sectional design, we collected walking parameters as 24 adults (M age= 46.19, SD= 12.90) with obese body mass index (BMI) scores (M BMI= 41.68, SD= 5.80) and 26 adults (M age= 21.88, SD= 3.48) with normal BMI scores (M BMI= 23.09, SD= 4.47) walked in 5 conditions for 5 trials each: on flat ground, crossing over low, medium, and high obstacles, and again on flat ground. The timing and distance of participants' steps were collected with a mechanized gait carpet (GAITRite, Inc.). We conducted 5 (condition) repeated measures (RM) ANOVAs on our main dependent variables, which measured how fast (velocity) and long (step length) participants' steps were and how much time they spent with one (single limb support time) versus two (double limb support time) feet on the ground. The results showed within session improvements in participants' walking patterns. Comparisons of the first and last trials on flat ground showed that participants took longer, faster steps by increasing step length and velocity (ps<.01). They also spent more time with one versus two feet on the ground via increased single limb support time and decreased double limb support time (ps<.001). Our findings suggest that an obstacle-crossing task may help spur improvements in walking patterns even before adults elect to undergo bariatric surgery
- …