Filippo Casamassima

A Wearable System for Gait Training in Subjects with Parkinson’s Disease

In this paper, a system for gait training and rehabilitation for Parkinsons disease (PD) patients in a daily life setting is presented. It is based on a wearable architecture aimed at the provision of real-time auditory feedback. Recent studies have, in fact, shown that PD patients can receive benefit from a motor therapy based on auditory cueing and feedback, as happens in traditional rehabilitation contexts with verbal instructions given by clinical operators. To this extent, a system based on a wireless body sensor network and a smartphone has been developed. The system enables real-time extraction of gait spatio-temporal features and their comparison with a patients reference walking parameters captured in the lab under clinical operator supervision. Feedback is returned to the user in form of vocal messages, encouraging the user to keep her/his walking behavior or to correct it. This paper describes the overall concept, the proposed usage scenario and the parameters estimated for the gait analysis. It also presents, in detail, the hardware-software architecture of the system and the evaluation of system reliability by testing it on a few subjects.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

EMG-based hand gesture recognition with flexible analog front end

Conditioning and processing of biological signals represent interesting challenges for wearable electronics in health applications. Information gathering from these signals requires complex hardware circuitry and dedicated computation resources. The design of innovative analog front-end integrated circuits, combined with efficient signal processing algorithms, allows the development of platforms for monitoring, activity and gesture recognition based on embedded real-time systems. This paper describes an Electromyography pattern recognition system based on the combination of low cost passive sensors, an innovative analog front-end and a low power microcontroller. The performance of the proposed system matches state-of-the-art high-end active sensors, opening the way to the development of affordable and accurate wearable devices.

A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal Gait Parameters

Gait impairments are among the most disabling symptoms in several musculoskeletal and neurological conditions, severely limiting personal autonomy. Wearable gait sensors have been attracting attention as diagnostic tool for gait and are emerging as promising tool for tutoring and guiding gait execution. If their popularity is continuously growing, still there is room for improvement, especially towards more accurate solutions for spatio-temporal gait parameters estimation. We present an implementation of a zero-velocity-update gait analysis system based on a Kalman filter and off-the-shelf shoe-worn inertial sensors. The algorithms for gait events and step length estimation were specifically designed to comply with pathological gait patterns. More so, an Android app was deployed to support fully wearable and stand-alone real-time gait analysis. Twelve healthy subjects were enrolled to preliminarily tune the algorithms; afterwards sixteen persons with Parkinsons disease were enrolled for a validation study. Over the 1314 strides collected on patients at three different speeds, the total root mean square difference on step length estimation between this system and a gold standard was 2.9%. This shows that the proposed method allows for an accurate gait analysis and paves the way to a new generation of mobile devices usable anywhere for monitoring and intervention.

Context aware power management for motion-sensing body area network nodes

Body Area Networks (BANs) are widely used mainly for healthcare and fitness purposes. In both cases, the lifetime of sensor nodes included in the BAN is a key aspect that may affect the functionality of the whole system. Typical approaches to power management are based on a trade-off between the data rate and the monitoring time. Our work introduces a power management layer capable to opportunistically use data sampled by sensors to detect contextual information such as user activity and adapt the node operating point accordingly. The use of this layer has been demonstrated on a commercial sensor node, increasing its battery lifetime up to a factor of 5.

Wearable audio-feedback system for gait rehabilitation in subjects with Parkinson's disease

Parkinsons disease (PD) is a degenerative neurological disorder, associated with movement impairments. Recent studies have shown that auxiliary cueing in the form of video, audio, or haptic feedback can improve the gait performance in PD patients. We have developed a new platform to support gait rehabilitation in PD patients based on a wearable system able to produce real-time feedback to the user in a comfortable and effective way. Using a set of custom wearable inertial sensors, with advanced on-board processing capabilities, our application employs a smartphone to analyze in real time the patients gait and to return an appropriate real time audio bio-feedback (ABF) message to the user to correct and improve gait performance. The main advantages of the system are mobility and unobtrusiveness: it can be comfortably worn and carried by the patient with no range restrictions, giving the possibility to monitor and rehabilitate the patient in real-life scenarios, both indoors and outdoors.

Power saving policies for multipurpose WBAN

Wireless Body Area Networks (WBAN) present a variety of Power Management challenges ranging from radio protocols, to node components. In this work we focus on optimal exploitation of low power operating points in micro-controller-based sensor nodes under sensor sampling frequency constraints. We introduce general techniques to link the selection of optimal operating points, and operating point transitions, to application-specific requirements. We then demonstrate the application of the general techniques on a real-life use case. Experimental results show that the general techniques are applicable in practice, even though significant case-specific tuning is required.

Energy-Efficient Context Aware Power Management with Asynchronous Protocol for Body Sensor Network

MEMS sensor technology and advances in electronics, low-power processors and communication have enabled ubiquitous monitoring, providing significant opportunities for a wide range of applications including wearable devices for fitness and health tracking. However, due to the limited form factor required, there remains a challenging issue that limits even more the success of wearable devices: the limited lifetime due to the small energy storages that supply the devices. This limitation affects usability and forces the data processing to keep low-complexity to match the power constraints. As wireless communication is typically the most power hungry activity in wearable sensors devices, many techniques focus on reducing the communication power consumption. For this reason, advanced power management can be exploited to increase the lifetime of the devices. In this work, we present a wireless body area network with an adaptive power management strategy combining an ultra-low power wake up radio with context awareness. The context aware power manager is based on activity recognition, which is evaluated to decide which other nodes must be activated. The nano-power wake up receiver is used to reduce the idle listening power of the main radio and enable an asynchronous ultra-low power protocol. In order to evaluate the benefit, we present a real world application to assist elderly people in gait rehabilitation through a closed loop feedback. Experimental results demonstrate the benefit of the proposed power management in terms of energy efficiency. We evaluate the overall power consumption of the system and the lifetime extension, which can increase up to a factor of 4 depending on the amount of time the system can be placed in sleep mode.

Digitally Controlled Feedback for DC Offset Cancellation in a Wearable Multichannel EMG Platform

Wearable systems capable to capture vital signs allow the development of advanced medical applications. One notable example is the use of surface electromyography (EMG) to gather muscle activation potentials, in principle an easy input for prosthesis control. However, the acquisition of such signals is affected by high variability and ground loop problems. Moreover, the input impedance influenced in time by motion and perspiration determines an offset, which can be orders of magnitude higher than the signal of interest. We propose a wearable device equipped with a digitally controlled Analog Front End (AFE) for biopotentials acquisition with zero-offset. The proposed AFE solution has an internal Digital to Analog Converter (DAC) used to adjust independently the reference of each channel removing any DC offset. The analog integrated circuit is coupled with a microcontroller, which periodically estimates the offset and implements a closed loop feedback on the analog part. The proposed approach was tested on EMG signals acquired from 4 subjects while performing different activities and shows that the system correctly acquires signals with no DC offset.

Context Aware Power Management Enhanced by Radio Wake Up in Body Area Networks

Wireless body area networks (WBANs) have the huge potential to enhance peoples lives. They are already present in many application domains, for instance sport and fitness, but they are wide spreading in particular in health and rehabilitation. However, there are still challenging issues that limit their wide diffusion in real life: primarily, the limited lifetime due to the batteries that usually supply the devices. This limitation affects usability and force the data processing to be simple to match the power constraints. This work tries to address the energy limitation by enabling both efficient and complex signal-processing applications and extension of lifetime. We present a power management strategy combining an ultra-low power wake up radio with context awareness. The context aware power manager based on activity recognition decides which nodes must be activated exploiting a nano-power wake up radio and power management policies. Result shows that by using both approaches it is possible to extend battery life of sensor nodes from few hours to an entire week.