Distinguishing Sitting and Standing Activities Using a Wearable Barometric Pressure Monitor

Recent advancements in wearable inertial sensor technology have enabled extensive research into activity classification. However, the distinction between the sitting and standing activities using trunk-worn wearable monitoring systems remains challenging, calling for additional sources of information such as altitude change. The aim of this study is to demonstrate the suitability of barometric pressure, an absolute estimate of sensor altitude, for distinguishing between sitting and standing (STS), as part of a wearable system. This barometric pressure sensor, the MS5611-BA01 from Measurement Specialties (CH), was integrated in a comfortable, miniaturized wearable inertial sensor device suitable for improved long-term activity recognition. The time evolution of the different modalities is displayed in Figure 1. A pilot study was conducted with 7 healthy volunteers (6 Males and 1 Female / Age: 27.8±2.1 years / BMI: 23.9±4.5 / Height: 1.80±0.075 m). Subjects were recorded performing a set of activities of daily-life including static postures (e.g., sitting or standing), postural transitions (e.g sit-to-stand, stand-to-sit) and dynamic activities (walking, climbing up/down stairs) in both indoor and outdoor real-world conditions. It enabled discriminating actual STS transitions from stationary postures with accuracies of 99.5% and 98% using averaging windows of Δt_average =4.16s and Δt_average =0.32s respectively. It is also capable of completely discriminating between Sit-to-Stand and Stand-to-Sit transitions.

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

Kamiar Aminian, Alan Kevin Bourke, Julien Chardonnens, Anisoara Ionescu, Fabien Massé

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 relative 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.

Elsevier2014

Physical activity monitoring based on wearable technology combining inertial and barometric pressure sensors

Fabien Massé

Stroke, or cerebral-vascular accident is the leading cause of disabilities in the western world. It primarily affects mobility due to a degradation of principal motor functions such as balance and gait. This reduction of mobility directly impacts the spectrum of possible activities of daily-living and consequently the level of independence. Patients undergo intensive physical therapy for improving various aspects of mobility. To evaluate the rehabilitation strategy outcome and guide patients through the therapy, clinical instruments have been widely used including questionnaires and motor function tests. Questionnaires related to mobility and quality of life depend on the interpretation, the cognitive abilities and the state-of-mind of the patient during the interview and are therefore subject to the interpretation of the patient. Motor function tests, such as Berg Balance Scale and Timed-Up-and-Go for evaluating balance and gait disorders, evaluates the patientâs motor capacity of performing given motor tasks in clinical settings, under close supervision. They are consequently not necessarily representative of the patientâs motor performance in daily life. Providing a measure of mobility performance would allow clinicians to better understand the impact, in patientâs home environment, of various rehabilitation strategies. Furthermore, emerging rehabilitation techniques such as virtual reality games may evaluate the patients during the training to build a progression scheme. However, the in-game assessment is limited to their scope of rehabilitation, i.e. it is restricted to one single component of mobility. To overcome the limitations of current assessment tools, an objective evaluation tool of stroke patientâs recovery in their daily-life environment is required. Recent miniaturization of wearable technologies have enabled new possibilities for long-term monitoring of physical activity in daily life. The detailed measurement of physical activity includes: the characterization of activity events such as postural (sit-to-stand and stand-to-sit) transitions; the quantification of the intensity and the amount of activities; and a pattern analysis of these events/activities through their distributions and temporal structures. The aim of this thesis is to provide an objective assessment tool of stroke patientsâ mobility performance in daily-life. According to the work performed, the manuscript is organized in four main parts: (i) selection of an appropriate barometric pressure sensor for improving postural transitions recognition, with validation on a group of healthy volunteers; (ii) improvement of a postural transition recognition in stroke patients using the selected barometric pressure sensor; (iii) design of an activity classification algorithm suitable for stroke patients by fusing barometric pressure and inertial sensor information; (iv) cross-sectional comparison (elderly controls vs stroke patients) of the mobility metrics (transition, activity, and pattern metrics) and their relationships with motor function tests. The wearable system devised in this thesis provides clinicians with a convenient (one sensor on the trunk) and more complete monitoring solution able to characterize the various aspects of strokeâs mobility in daily-life. This solution can be used to objectively and accurately assess the rehabilitation effectiveness and eventually to adapt/fit the rehabilitation program to the patient's needs.

EPFL2015

Physical activity monitoring based on wearable technology combining inertial and barometric pressure sensors

Fabien Massé

EPFL2015