A tool for linguistic assessment of rehabilitation exercises

Fuzzy Sets and Systems, 2015

Self-tracking our physical activity allows us to acquire a better self-knowledge about our physical, and even mental health condition. Currently, the description of the time series provided by sensors is done by means of graphics and tables, that are hard to interpret by non-expert humans. Here, we present a computational application that dynamically describes in natural language the physical activity. The final reports are adapted to the everyday language and user's needs. The application highlights the relevant information obtained at different levels of temporal detail. We have included experimental results that demonstrate the flexibility and applicability of the new tool.

Mechatronic Systems and Control (formerly Control and Intelligent Systems)

There is evidence that many cognitive conditions affect the human motor system. Gait analysis has lately been used as a means of studying this physical-cognitive correlation. The development of gait analysis systems, able to record and analyze gait during normal daily activities and in uncontrolled environment, is an important addition to this area of research. Lately, linguistic approaches have been studied as means to achieve activity classification from vision sensors. The present work aims to extend the linguistic approach to achieve quantitative analysis of gait from accelerometer data. The proposed method can be used to extend the Human Activity Language framework to include the analysis of inertial sensors such as accelerometers. Results show that the proposed method is more accurate and robust than previous methods and can be used to extract a number of clinically relevant gait measurements. A novel symmetry index is presented to exemplify how the proposed method is able to extract more information from accelerometer signals than previous methods.

Communications in Computer and Information Science, 2014

The design of automated systems for the recognition of specific human activities is among the most promising research activities in Ambient Intelligence. The literature suggests the adoption of wearable devices, relying on acceleration information to model the activities of interest and distance metrics for the comparison of such models with the run-time data. Most current solutions do not explicitly model the uncertainty associated with the recognition, but rely on crisp thresholds and comparisons which introduce brittleness and inaccuracy in the system. We propose a framework for the recognition of simple activities in which recognition uncertainty is modelled using possibility distributions. We show that reasoning about this explicitly modelled uncertainty leads to a system with enhanced recognition accuracy and precision.

2008

This paper addresses the problem of human activity monitoring including data about the degree of accumulated mental stress. We describe a simple experiment aimed at exploring the possibilities of providing people with linguistic feedback of the temporal evolution of their stress and concentration capability during the working day. We provide details about the utilized sensors and describe how to use fuzzy logic and a finite states machine to create a linguistic model of the evolution of these parameters with the time. Some experimental results are included.

2020

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approac...

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), 2014

In this paper, we describe a rule based approach to realtime motion assessment of rehabilitation exercises. We use three types of rules to define each exercise: (1) dynamic rules, with each rule specifying a sequence of monotonic segments of the moving joint or body segment, (2) static rules for stationary joints or body segments, and (3) invariance rules that dictate the requirements of moving joints or body segments. A finite state machine based approach is used in dynamic rule specification and realtime assessment. In addition to the typical advantages of the rule based approach, such as realtime motion assessment with specific feedback, our approach has the following advantages:

Scientific Data, 2022

a database of physical therapy exercises with variability of execution collected by wearable sensors Sara García-de-Villa ✉ , ana Jiménez-Martín & Juan Jesús García-Domínguez this document introduces the PHYtMO database, which contains data from physical therapies recorded with inertial sensors, including information from an optical reference system. PHYtMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. PHYTMO includes magneto-inertial data, together with a highly accurate location and orientation in the 3D space provided by the optical system. The files were stored in CSV format to ensure its usability. the aim of this dataset is the availability of data for two main purposes: the analysis of techniques for the identification and evaluation of exercises using inertial sensors and the validation of inertial sensor-based algorithms for human motion monitoring. Furthermore, the database stores enough data to apply Machine Learning-based algorithms. the participants' age range is large enough to establish age-based metrics for the exercises evaluation or the study of differences in motions between different groups.

2000

IEEE transactions on medical robotics and bionics, 2024

Multimodal physiological monitoring and related estimation of the PsychoPhysiological (PP) state play an essential role in investigating the physical and cognitive workload of people executing a motor task. The aim of this work was to develop a data-driven Fuzzy Logic method to estimate four PP indicators, i.e. Energy Expenditure, Fatigue, Attention, and Stress, and test it in a study including ten healthy participants walking while assisted by a lower limb treadmill-based exoskeleton. PP indicators were compared with participants' self-reported evaluation of the human-robot interaction experience following the administration of a dedicated questionnaire. Results from a correlation analysis demonstrated that the output of the Fuzzy Logic method was consistent with the participants' subjective assessment.

Fuzzy Systems, 2008. …, 2008

The understanding of human motions captured in image sequences pose two main difficulties which are often regarded as computationally ill-defined: 1) modelling the uncertainty in the training data, and 2) constructing a generic activity representation that can describe simple actions as well as complicated tasks that are performed by different humans. In this paper, these problems are addressed from a direction which utilises the concept of fuzzy qualitative robot kinematics . First of all, the training data representing a typical activity is acquired by tracking the human anatomical landmarks in an image sequences. Then, the uncertainty arise when the limitations of the tracking algorithm are handled by transforming the continuous training data into a set of discrete symbolic representationsqualitative states in a quantisation process. Finally, in order to construct a template that is regarded as a combination ordered sequence of all body segments movements, robot kinematics, a well-defined solution to describe the resulting motion of rigid bodies that form the robot, has been employed. We defined these activity templates as qualitative normalised templates, a manifold trajectory of unique state transition patterns in the quantity space. Experimental results and a comparison with the hidden Markov models have demonstrated that the proposed method is very encouraging and shown a better successful recognition rate on the two available motion databases.