Guglielmo Cola

A smartphone-based fall detection system

Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a smartphone-based fall detection system that monitors the movements of patients, recognizes a fall, and automatically sends a request for help to the caregivers. To reduce the problem of false alarms, the system includes novel techniques for the recognition of those activities of daily living that could be erroneously mis-detected as falls (such as sitting on a sofa or lying on a bed). To limit the intrusiveness of the system, a small external sensing unit can also be used for the acquisition of movement data.

Recognition of false alarms in fall detection systems

Falls are a major cause of hospitalization and injury-related deaths among the elderly population. The detrimental effects of falls, as well as the negative impact on health services costs, have led to a great interest on fall detection systems by the health-care industry. The most promising approaches are those based on a wearable device that monitors the movements of the patient, recognizes a fall and triggers an alarm. Unfortunately such techniques suffer from the problem of false alarms: some activities of daily living are erroneously reported as falls, thus reducing the confidence of the user. This paper presents a novel approach for improving the detection accuracy which is based on the idea of identifying specific movement patterns into the acceleration data. Using a single accelerometer, our system can recognize these patterns and use them to distinguish activities of daily living from real falls; thus the number of false alarms is reduced.

An On-Node Processing Approach for Anomaly Detection in Gait

A novel method is proposed for capturing deviation in gait using a wearable accelerometer. Previous research has outlined the importance of gait analysis to assess frailty and fall risk in elderly patients. Several solutions, based on wearable sensors, have been proposed to assist geriatricians in mobility assessment tests, such as the Timed Up-and-Go test. However, these methods can only be applied to supervised scenarios and do not allow continuous and unobtrusive monitoring of gait. The method we propose is designed to achieve continuous monitoring of gait in a completely unsupervised fashion, requiring the use of a single waist-mounted accelerometer. The users gait patterns are automatically learned using specific acceleration-based features, while anomaly detection is used to capture subtle changes in the way the user walks. All the required processing can be executed in real time on the wearable device. The method was evaluated with 30 volunteers, who simulated a knee flexion impairment. On average, our method obtained ~84% accuracy in the recognition of abnormal gait segments lasting ~5 s. Prompt detection of gait anomalies could enable early intervention and prevent falls.

Gait-based authentication using a wrist-worn device

Every individual has a distinctive way of walking. For this reason gait can be a key element of biometric techniques aimed at authenticating and/or identifying the user of a wearable device. This paper presents a lightweight method that uses the acceleration collected at the users wrist for authentication purposes. The users typical gait pattern is learned during the first period of use, then detection of anomalies in a set of acceleration-based features is used to understand if a new user, a possible impostor or a thief, is wearing the device. The method has been successfully evaluated with 15 volunteers, showing an Equal Error Rate of 2.9%. These results suggest that gait-based authentication with a wrist-worn device can be carried out with high accuracy levels. A comparison with a similar method executed on a pocket-worn device is also included.

An unsupervised approach for gait-based authentication

Similar to fingerprint and iris pattern, everyones gait is unique, and gait has been proposed as a biometric feature for security applications. This paper presents a lightweight accelerometer-based technique for user authentication on smart wearable devices. Designed as an unsupervised classification approach, the proposed authentication technique can learn the users gait pattern automatically when the user first starts wearing the device. Anomaly detection is then used to verify the device owner. The technique has been evaluated both in controlled and uncontrolled environments, with 20 and 6 healthy volunteers respectively. The Equal Error Rate (EER) in the controlled environments ranged from 5.7% (waist-mounted sensor) to 8.0% (trouser pocket). In the uncontrolled experiment, the device was put in the subjects trouser pocket, and the results were similar to the respective supervised experiment (EER=9.7%).

Posture Recognition Using the Interdistances Between Wearable Devices

Recognition of users postures and activities is particularly important, as it allows applications to customize their operations according to the current situation. The vast majority of available solutions are based on wearable devices equipped with accelerometers and gyroscopes. In this article, a different approach is explored: The posture of the user is inferred from the interdistances between the set of devices worn by the user. Interdistances are first measured by using ultra-wideband transceivers operating in two-way ranging mode and then provided as input to a classifier that estimates current posture. An experimental evaluation shows that the proposed method is effective (up to

Real-Time Identification Using Gait Pattern Analysis on a Standalone Wearable Accelerometer

\sim 98.2\%

Fall detection using ultra-wideband positioning

accuracy), especially when using a personalized model. The method could be used to enhance the accuracy of activity recognition systems based on inertial sensors.

Personalized gait detection using a wrist-worn accelerometer

Wearable devices can gather sensitive information about their users. For this reason, automated authentication and identification techniques are increasingly adopted to ensure security and privacy. Furthermore, identification can be used to automatically customize operations according to the needs of the current user. A gait-based identification method that can be executed in real time on devices with limited resources is here presented. The method exploits a wearable accelerometer to continuously analyze the user’s gait pattern and perform identification. Experiments were conducted with ten volunteers, who carried the device in a trouser pocket and followed their daily routine without predefined constraints. In total, ∼ 98 hours of acceleration traces were collected in uncontrolled environment, including 3073 gait segments. User identification results show a recognition rate ranging from 95% to 100%, depending on the mode of operation. It is demonstrated that the method can be executed on a standalone device with less than 8 KB of RAM. In addition, the energy consumption is evaluated and compared with an architecture that requires the presence of an external computing unit. Results show that the proposed solution significantly improves the lifetime of the device (approximately +70% for the considered platform), hence fostering user acceptance.

Smart Shoe-Based Evaluation of Gait Phase Detection Accuracy Using Body-Worn Accelerometers

Falls are a major health problem in our aging society. Fall detection systems are aimed at automatically sending an alarm in case of falls. Unfortunately most of the systems currently available, which use accelerometric sensors, are characterized by a relatively large number of false alarms. In fact, many activities of daily living may produce fall-like acceleration signals. We propose a method that uses ultra-wideband positioning to track the movements of the user and detect falls. Preliminary results show that the approach is reliable in detecting falls and simple postures.