Freezing of gait (FOG) is a leading cause of falls and fractures in Parkinson’s disease (PD). The episodic and rather unpredictable occurrence of FOG, coupled with the variable response to l-DOPA of this gait disorder, makes the objective evaluation of FOG severity a major clinical challenge in the therapeutic management of patients with PD. The aim of this study was to examine and compare gait, clinically and objectively, in patients with PD, with and without FOG, by means of a new wearable system. We also assessed the effect of l-DOPA on FOG severity and specific spatiotemporal gait parameters in patients with and without FOG. To this purpose, we recruited 28 patients with FOG, 16 patients without FOG, and 16 healthy subjects. In all participants, gait was evaluated clinically by video recordings and objectively by means of the wearable wireless system, during a modified 3-m Timed Up and Go (TUG) test. All patients performed the modified TUG test under and not under dopaminergic therapy (ON and OFF therapy). By comparing instrumental data with the clinical identification of FOG based on offline video-recordings, we also assessed the performance of the wearable system to detect FOG automatically in terms of sensitivity, specificity, positive and negative predictive values, and finally accuracy. TUG duration was longer in patients than in controls, and the amount of gait abnormalities was prominent in patients with FOG compared with those without FOG. l-DOPA improved gait significantly in patients with PD and particularly in patients with FOG mainly by reducing FOG duration and increasing specific spatiotemporal gait parameters. Finally, the overall wireless system performance in automatic FOG detection was characterized by excellent sensitivity (93.41%), specificity (98.51%), positive predictive value (89.55%), negative predictive value (97.31%), and finally accuracy (98.51%). Our study overall provides new information on the beneficial effect of l-DOPA on FOG severity and specific spatiotemporal gait parameters as objectively measured by a wearable sensory system. The algorithm here reported potentially opens to objective long-time sensing of FOG episodes in patients with PD.

A wearable wireless sensing system for assisting 1 patients affected by Parkinson's disease is proposed. It uses inte-2 grated micro-electro-mechanical inertial sensors able to recognize 3 the episodes of involuntary gait freezing. The system operates in 4 real time and is designed for outdoor and indoor applications. 5 Standard tests were performed on a noticeable number of 6 patients and healthy persons and the algorithm demonstrated 7 its reliability and robustness respect to individual specific gait 8 and postural behaviors. The overall performances of the system 9 are excellent with a specificity higher than 97%.

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It is proposed a wearable sensing system based on Inertial Measurement Units (IMUs) for the long-time detection of specific human motion disorders. The system uses a single sensor positioned on the head, close to the ear. The system recognizes noticeable gait features as irregular steps and the gait block (freezing of gait). Respect to other positions on the body, the headset has the maximum sensitivity to the trunk oscillations which patients make to get out of the block, increasing dramatically the risk of falls. The headset has also the advantage that it is easy to wear and the whole system can be contained in a single package. In fact, an audio device for auditory feedback to the patient can be integrated without any wireless/wired connection to the ear. The classification of those motion features is performed by an artificial neural network (ANN) and starts from the raw signals collected by the IMU. The ANN algorithm of recognition is extremely versatile and works for any individual gait features. The ANN allows robust and reliable detection of the targeted kinetic features and requires fast and light calculations.In this paper, it is presented the recognition of irregular steps, trunk oscillations and stop state obtained performing calculations out-board on a PC, without losing the generality of the method validity. The final headset system will be extremely energy efficient thanks to its compactness, to the fact that the ANN avoids computational energy wasting, and that the audio feedback does not require any wired/wireless connection. This affects positively the system performance in terms of power consumption and battery life (monitoring time).

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We propose two different wearable wireless sensing systems based on Inertial Measurement Units for the home monitoring of specific symptoms of the Parkinson's disease. In one configuration just one sensor is inserted in a headset, in the other configuration two sensors are positioned on the patient's shins. They recognize and classify noticeable motion disorders potentially dangerous for patients and give an audio feedback. The systems use dedicated algorithms for real time processing of the raw signals from accelerometers and gyroscopes, one of which is based on an artificial neural network and another on a timebased analysis. The headset system detects satisfactorily a wide class of motion irregularities including the trunk disorders, but is poorly reliable on Parkinson's patients. The other system with sensors on the shins provides an early detection of the freezing of gait with excellent performance in terms of sensitivity and precision, and timely provides a rhythmic auditory stimulation to the patient for releasing the involuntary block state.

It is proposed an electronic system for the long-time monitoring of specific motor symptoms in patients affected by Parkinson's Disease while being at home and making their usual daily activity. The system is made of a network of non-invasive wireless inertial sensors fixed on the patient body. The muscles activity is contemporarily analysed through the integration of a circuit for the surface electromyography. Postprocessing algorithms quantify movements in terms of amplitude and power spectrum. Data are electronically elaborated and wireless transmitted to a receiver in the patient home, to be accessed remotely by doctors. The challenge is the automatic distinction between specific parkinsonian symptoms including resting tremor and freezing of gait and patient's voluntary movements made in daily life. To the aim, the contemporarily analysis of muscle activity becomes necessary in specific situations, as in the case of freezing of gait, where accelerometers signals may be misleading. Goal of this research is the comprehension of all the possible environmental and individual factors which favor worsening of gait disorders during the patient daily life and the customization of the drug therapy, aiming to preventing catastrophic events such as falls. Results shown here refer to upper limb tremor and freezing of gait.

Abstract:A non-invasive wearable wireless sensing system for assisting patients affected by Parkinson's Disease is proposed. It uses inertial sensors to recognize gait states and involuntary gait freezing. The system is designed for indoor and outdoor long-time monitoring and realizes an individual electronic diary useful for doctors for estimating better the stage of the disease and customizing the pharmacological therapy. Standard tests were performed on a noticeable number of patients. The system performances are the state-of-art in the detection of specific movement disorders of PD patients. The algorithm demonstrated its reliability and robustness respect to individual specific gait and postural behaviors.