Fabien Massé

Suitability of commercial barometric pressure sensors to distinguish sitting and standing activities for wearable monitoring

Despite its medical relevance, accurate recognition of sedentary (sitting and lying) and dynamic activities (e.g. standing and walking) remains challenging using a single wearable device. Currently, trunk-worn wearable systems can differentiate sitting from standing with moderate success, as activity classifiers often rely on inertial signals at the transition period (e.g. from sitting to standing) which contains limited information. Discriminating sitting from standing thus requires additional sources of information such as elevation change. The aim of this study is to demonstrate the suitability of barometric pressure, providing an absolute estimate of elevation, for evaluating sitting and standing periods during daily activities. Three sensors were evaluated in both calm laboratory conditions and a pilot study involving seven healthy subjects performing 322 sitting and standing transitions, both indoor and outdoor, in real-world conditions. The MS5611-BA01 barometric pressure sensor (Measurement Specialties, USA) demonstrated superior performance to counterparts. It discriminates actual sitting and standing transitions from stationary postures with 99.5% accuracy and is also capable to completely dissociate Sit-to-Stand from Stand-to-Sit transitions.

Wearable Barometric Pressure Sensor to Improve Postural Transition Recognition of Mobility-Impaired Stroke Patients

Sit-to-stand and Stand-to-sit transfers (STS) provide relevant information regarding the functional limitation of mobility-impaired patients. The characterization of STS pattern using a single trunk fixed inertial sensor has been proposed as an objective tool to assess changes in functional ability and balance due to disease. Despite significant research efforts, STS quantification remains challenging due to the high inter- and between- subject variability of this motion pattern. The present study aims to improve the performance of STS detection and classification by fusing the information from barometric pressure (BP) and inertial sensors while keeping a single sensor located at the trunk. A total number of 345 STSs were recorded from 12 post-stroke patients monitored in a semi-structured conditioned protocol. Model-based features of BP signal were combined with kinematic parameters from accelerometer and/or gyroscope and used in a logistic regression-based classifier to detect STS and then identify their types. The correct classification rate was 90.6% with full sensor (BP and inertial) configuration and 75.4% with single inertial sensor. Receiver-Operating-Characteristics analysis was carried out to characterize the robustness of the models. The results demonstrate the potential of BP sensor to improve the detection and classification of STSs when monitoring is performed unobtrusively in every-day life.

Lifestyle Evaluation Using Wearable Technologies: Opportunities for Stroke Patients

Stroke is the leading cause of disabilities in the western world and may cause severe motor impairments. In the current clinical practice, progress during physical rehabilitation is assessed by a therapist for specific motor functions and through questionnaires that address patient’s general well-being or lifestyle. However, this approach is not only prone to subjectivity due to both the patient and the rater during the assessment, but it is also non-continuous and coarse grained, since it can only be administered every few months. In this paper, the concept of a complementary solution based on wearable technologies is proposed, and the remaining challenges to provide an objective, fine-grained and long-term evaluation of patient’s lifestyle through physical activity monitoring are highlighted.

Measuring upper limb function in children with hemiparesis with 3D inertial sensors

PurposeUpper limb assessments in children with hemiparesis rely on clinical measurements, which despite standardization are prone to error. Recently, 3D movement analysis using optoelectronic setups has been used to measure upper limb movement, but generalization is hindered by time and cost. Body worn inertial sensors may provide a simple, cost-effective alternative.MethodsWe instrumented a subset of 30 participants in a mirror therapy clinical trial at baseline, post-treatment, and follow-up clinical assessments, with wireless inertial sensors positioned on the arms and trunk to monitor motion during reaching tasks.ResultsInertial sensor measurements distinguished paretic and non-paretic limbs with significant differences (P < 0.01) in movement duration, power, range of angular velocity, elevation, and smoothness (normalized jerk index and spectral arc length). Inertial sensor measurements correlated with functional clinical tests (Melbourne Assessment 2); movement duration and complexity (Higuchi fractal dimension) showed moderate to strong negative correlations with clinical measures of amplitude, accuracy, and fluency.ConclusionInertial sensor measurements reliably identify paresis and correlate with clinical measurements; they can therefore provide a complementary dimension of assessment in clinical practice and during clinical trials aimed at improving upper limb function.