BackgroundStroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients’ mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor.MethodsOur study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic.Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH).ResultsThe algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation.ConclusionThe monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier.

This paper presents a novel technique to predict freezing of gait in advance-stage Parkinsonian patients using movement data from wearable sensors. A two-class approach is presented which consists of autoregressive predictive models to project the feature time series, followed by machine learning based classifiers to discriminate freezing from nonfreezing based on the predicted features. To implement and validate our technique a set of time domain and frequency domain features were extracted from the 3D acceleration data, which was then analyzed using information theoretic and feature selection approaches to determine the most discriminative features. Predictive models were trained to predict the features from their past values, then fed into binary classifiers based on support vector machines and probabilistic neural networks which were rigorously cross validated. We compared the results of this approach with a three-class classification approach proposed in previous literature, in which a pre-freezing class was introduced and the problem of prediction of the gait freezing incident was reduced to solving a three-class classification problem. The twoclass approach resulted in a sensitivity of 93±4%, specificity of 91±6%, with an expected prediction horizon of 1.72 seconds. Our subject-specific gait freezing prediction algorithm outperformed existing algorithms, yields consistent results across different subjects and is robust against the choice of classifier, with slight variations in the selected features. In addition, we analyzed the merits and limitations of different families of features to predict gait freezing.

Abstract-Wearable devices to assist abnormal gaits require controllers that interact with the user in an intuitive and unobtrusive manner. To design such a controller, we investigated a bio-inspired walking controller for orthoses and prostheses. We present (i) a Simulink neuromuscular control library derived from a computational model of reflexive neuromuscular control of human gait with a central pattern generator (CPG) extension, (ii) an ankle reflex controller for the Achilles exoskeleton derived from the library, and (iii) the mechanics and energetics of healthy subjects walking with an actuated ankle orthosis using the proposed controller. As this controller was designed to mimic human reflex patterns during locomotion, we hypothesize that walking with this controller would lead to lower energetic costs, compared to walking with the added mass of the device only, and allow for walking at different speeds without explicit control. Preliminary results suggest that the neuromuscular controller does not disturb walking dynamics in both slow and normal walking cases and can also reduce the net metabolic cost compared to transparent mode of the device. Reductions in tibialis anterior and soleus activity were observed, suggesting the controller could be suitable, in future work, for augmenting or replacing normal walking functions. We also investigated the impedance patterns generated by the neuromuscular controller. The validity of the equivalent variable impedance controller, particularly in stance phase, can facilitate serving subject-specific features by linking impedance measurement and neuromuscular controller.

Knee implant loosening is mainly caused by the weakness of the prosthesis-bone interface and is the main reason for surgical revisions. However, pre-operative diagnosis is difficult due to lack of accurate tests. In this study, we developed a vibration-based system to detect the loosening of the tibial implant of an instrumented knee prosthesis. The proposed system includes an instrumented vibrator for transcutaneous stimulation of the bone in a repeatable manner, and accelerometer sensors integrated into the implants to measure the propagated vibration. A coherence-based detection technique was proposed to distinguish the loosened implants from the secure ones. Fourteen ex vivo lower limbs were used, on which the knee prosthesis was implanted, and harmonic-forced vibration was applied on the tibia. The input–output coherence measure provided 92.26% accuracy, a high sensitivity (91.67%) and specificity (92.86%). This technique was benchmarked against power spectrum based analysis of the propagated vibration to the implant. In particular, loosening detection based on new peak appearance, peak shift, and peak flattening in power spectra showed inferior performance to the proposed coherence-based technique. As such, application of vibration on our instrumented knee prosthesis together with input–output coherence analysis enabled us to distinguish the secure from loose implants.

Spasticity, a common symptom in patients with upper motor neuron lesions, reduces the ability of a person to freely move their limbs by generating unwanted reflexes. Spasticity can interfere with rehabilitation programs and cause pain, muscle atrophy and musculoskeletal deformities. Despite its prevalence, it is not commonly understood. Widely used clinical scores are neither accurate nor reliable for spasticity assessment and follow up of treatments. Advancement of wearable sensors, signal processing and robotic platforms have enabled new developments and modeling approaches to better quantify spasticity. In this paper, we review quantitative modeling techniques that have been used for evaluating spasticity. These models generate objective measures to assess spasticity and use different approaches, such as purely mechanical modeling, musculoskeletal and neurological modeling, and threshold control-based modeling. We compare their advantages and limitations and discuss the recommendations for future studies. Finally, we discuss the focus on treatment and rehabilitation and the need for further investigation in those directions.

This paper describes the development of a polyimide-based MEMS strain-sensing device. Finite element analysis was used to investigate an artificial knee implant and assist on device design and to optimize sensing characteristics. The sensing element of the device was fabricated using polyimide micromachining with embedded thin-metallic wires and placed into a knee prosthesis. The device was evaluated experimentally in a mechanical knee simulator using static and dynamic axial load conditions similar to those encountered in vivo. Results indicates the sensor is capable of measuring the strain associated to the total axial forces in the range of approximately 4 times body weight with a good sensitivity and accuracy for events happening within 1 s time window.

Abstract-This work presents an accurate, robust, wearable measurement system for foot clearance estimation along with algorithms to provide a real-time estimate of foot height and orientation. Different configurations of infrared distance meter sensors were used, both alone and in combination with an inertial measurement unit. In order to accurately estimate the foot clearance when in presence of daylight and when the foot orientation changes dynamically during walking, several algorithms were designed based on physics of sensors and tuned using the acquired data against a reference system. These algorithms, specific to the number of sensors, include the estimators of the foot orientation and estimators of the foot clearance. These estimators are tested on normal walking (RMS error ≤ 8.4mm) and walking with exaggerated step heights and inversion-eversion rotations. A Bayesian fusion of estimators was also implemented to better cope with the extreme and abnormal walking kinematics while maintaining a high performance for normal walking. All estimators were trained on uniformly distributed bootstrapped sub-samples of data and tested on several normal and abnormal walking data. The results proved the robustness of the proposed system against variations in the gait kinematics (|mean| ± standard deviation of error for heel and toe clearance was equal to or smaller than 3.1±9.3 mm when using a Bayesian fusion of three different estimators) and environment lighting (with an introduced error of 1 to 4% of actual distance).Index Terms-Foot clearance, infrared range meter, inertial measurement unit, Bayesian fusion.

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