Human motion analysis is crucial for a wide range of applications and disciplines. The development and validation of low cost and unobtrusive sensing systems for ambulatory motion detection is still an open issue. Inertial measurement systems and e-textile sensors are emerging as potential technologies for daily life situations. We developed and conducted a preliminary evaluation of an innovative sensing concept that combines e-textiles and tri-axial accelerometers for ambulatory human motion analysis. Our sensory fusion method is based on a Kalman filter technique and combines the outputs of textile electrogoniometers and accelerometers without making any assumptions regarding the initial accelerometer position and orientation. We used our technique to measure the flexion-extension angle of the knee in different motion tasks (monopodalic flexions and walking at different velocities). The estimation technique was benchmarked against a commercial measurement system based on inertial measurement units and performed reliably for all of the various tasks (mean and standard deviation of the root mean square error of 1:96 and 0:96, respectively). In addition, the method showed a notable improvement in angular estimation compared to the estimation derived by the textile goniometer and accelerometer considered separately. In future work, we will extend this method to more complex and multi-degree of freedom joints.

This paper presents an innovative wearable kinesthetic glove realized with knitted piezoresistive fabric (KPF) sensor technology. The glove is conceived to capture hand movement and gesture by using KPF in a double-layer configuration working as angular sensors (electrogoniometers). The sensing glove prototype is endowed by three KPF goniometers, used to track flexion and extension movement of metacarpophalangeal joint of thumb, index, and middle fingers. The glove is devoted to the continuous monitoring of patients during their daily-life activities, in particular for stroke survivors during their rehabilitation. The prototype performances have been evaluated in comparison with an optical tracking system considered as a gold standard both for relieving static and dynamic posture and gesture of the hand. The introduced prototype has shown very interesting figures of merit. The angular error, evaluated through the standard Bland Altman analysis, has been estimated in ${\bf \pm 3^\circ}$ which is slightly less accurate than commercial electrogoniometers. Moreover, a new conceptual prototype design, preliminary evaluated within this study, is presented and discussed in order to solve actual limitations in terms of number and type of sensor connections, avoiding mechanical constraints given by metallic inextensible wires and improving user comfort.

Monitoring joint angles through wearable systems enables human posture and gesture to be reconstructed as a support for physical rehabilitation both in clinics and at the patient’s home. A new generation of wearable goniometers based on knitted piezoresistive fabric (KPF) technology is presented.

Electrical Impedance Tomography (EIT) is a medical imaging technique that has been recently used to realize stretchable pressure sensors. In this method, voltage measurements are taken at electrodes placed at the boundary of the sensor and are used to reconstruct an image of the applied touch pressure points. The drawback with EIT-based sensors, however, is their low spatial resolution due to the ill-posed nature of the EIT reconstruction. In this paper, we show our performance evaluation of different EIT drive patterns, specifically strategies for electrode selection when performing current injection and voltage measurements. We compare voltage data with Signal-to-Noise Ratio (SNR) and Boundary Voltage Changes (BVC), and study image quality with Size Error (SE), Position Error (PE) and Ringing (RNG) parameters, in the case of one-point and two-point simultaneous contact locations. The study shows that, in order to improve the performance of EIT based sensors, the electrode selection strategies should dynamically change correspondingly to the location of the input stimuli. In fact, the selection of one drive pattern over another can improve the target size detection and position accuracy up to 4.7% and 18%, respectively.

Monitoring physical activities during post-stroke rehabilitation in daily life may help physicians to optimize and tailor the training program for patients. The European research project INTERACTION (FP7-ICT-2011-7-287351) evaluated motor capabilities in stroke patients during the recovery treatment period. We developed wearable sensing platform based on the sensor fusion among inertial, knitted piezoresistive sensors and textile EMG electrodes . The device was conceived in modular form and consists of a separate shirt, trousers, glove and shoe. Thanks to the novel fusion approach it has been possible to develop a model for the shoulder taking into account the scapulo-thoracic joint of the scapular girdle, considerably improving the estimation of the hand position in reaching activities. In order to minimize the sensor set used to monitor gait, a single inertial sensor fused with a textile goniometer proved to reconstruct the orientation of all the body segments of the leg. Finally, the sensing glove, endowed with three textile goniometers and three force sensors showed good capabilities in the reconstruction of grasping activities and evaluating the interaction of the hand with the environment, according to the project specifications. This paper reports on the design and the technical evaluation of the performance of the sensing platform, tested on healthy subjects.

According to current trends in healthcare sensing technologies, we describe a textile-based pressure sensing matrix that can be integrated in the mattress of a smart bed to characterize sleeping posture/movement of a subject and to extract breathing activity. The pressure mapping layer is developed as a matrix of 195 piezoresistive sensors, it is entirely made of textile materials, and it is the basic component of a smart bed that can perform sleep analysis, can extract physiological parameters, and can detect environmental data related to subject’s health. In this paper, we show the principle of the pressure mapping layer and the architecture of the dedicated electronic system that we developed for signal acquisition. In addition, we describe the algorithms for posture/movement classification (dedicated artificial neural network) and for extraction of the breathing rate (frequency domain analysis). We also perform validation of the system to quantify the accuracy/precision of the posture classification and the statistical analysis to compare our breathing rate estimation with the gold standard.