5,530 research outputs found
SensX: About Sensing and Assessment of Complex Human Motion
The great success of wearables and smartphone apps for provision of extensive
physical workout instructions boosts a whole industry dealing with consumer
oriented sensors and sports equipment. But with these opportunities there are
also new challenges emerging. The unregulated distribution of instructions
about ambitious exercises enables unexperienced users to undertake demanding
workouts without professional supervision which may lead to suboptimal training
success or even serious injuries. We believe, that automated supervision and
realtime feedback during a workout may help to solve these issues. Therefore we
introduce four fundamental steps for complex human motion assessment and
present SensX, a sensor-based architecture for monitoring, recording, and
analyzing complex and multi-dimensional motion chains. We provide the results
of our preliminary study encompassing 8 different body weight exercises, 20
participants, and more than 9,220 recorded exercise repetitions. Furthermore,
insights into SensXs classification capabilities and the impact of specific
sensor configurations onto the analysis process are given.Comment: Published within the Proceedings of 14th IEEE International
Conference on Networking, Sensing and Control (ICNSC), May 16th-18th, 2017,
Calabria Italy 6 pages, 5 figure
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
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Deep neural networks, including recurrent networks, have been successfully
applied to human activity recognition. Unfortunately, the final representation
learned by recurrent networks might encode some noise (irrelevant signal
components, unimportant sensor modalities, etc.). Besides, it is difficult to
interpret the recurrent networks to gain insight into the models' behavior. To
address these issues, we propose two attention models for human activity
recognition: temporal attention and sensor attention. These two mechanisms
adaptively focus on important signals and sensor modalities. To further improve
the understandability and mean F1 score, we add continuity constraints,
considering that continuous sensor signals are more robust than discrete ones.
We evaluate the approaches on three datasets and obtain state-of-the-art
results. Furthermore, qualitative analysis shows that the attention learned by
the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable
Computers (ISWC) 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
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