Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Single Sensor <s> A new method of evaluating the characteristics of postural transition (PT) and their correlation with falling risk in elderly people is described. The time of sit-to-stand and stand-to-sit transitions and their duration were measured using a miniature gyroscope attached to the chest and a portable recorder placed on the waist. Based on a simple model and the discrete wavelet transform, three parameters related to the PT were measured, namely, the average and standard deviation of transition duration and the occurrence of abnormal successive transitions (number of attempts to have a successful transition). The comparison between two groups of elderly subjects (with high and low fall-risk) showed that the computed parameters were significantly correlated with the falling risk as determined by the record of falls during the previous year, balance and gait disorders (Tinetti score), visual disorders, and cognitive and depressive disorders (p < 0.01). In this study, the wavelet transform has provided a powerful technique for enhancing the pattern of PT, which was mainly concentrated into the frequency range of 0.04-0.68 Hz. The system is especially adapted for long-term ambulatory monitoring of elderly people. <s> BIB001 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Single Sensor <s> A system for capturing habitual, in-home gait measurements using an environmentally mounted depth camera, the Microsoft Kinect, is presented. Previous work evaluating the use of the Kinect sensor for in-home gait measurement in a lab setting has shown the potential of this approach. In this paper, a single Kinect sensor and computer were deployed in the apartments of older adults in an independent living facility for the purpose of continuous, in-home gait measurement. In addition, a monthly fall risk assessment protocol was conducted for each resident by a clinician, which included traditional tools such as the timed up a go and habitual gait speed tests. A probabilistic methodology for generating automated gait estimates over time for the residents of the apartments from the Kinect data is described, along with results from the apartments as compared to two of the traditionally measured fall risk assessment tools. Potential applications and future work are discussed. <s> BIB002 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Single Sensor <s> Abstract A non-invasive technique for posture classification suitable to be used in several in-home scenarios is proposed and preliminary validation results are presented. 3D point cloud sequences were acquired using a single time-of-flight sensor working in a privacy preserving modality and they were processed with a low power embedded PC. In order to satisfy different application requirements (e.g. covered distance range, processing speed and discrimination capabilities), a twofold discrimination approach was investigated in which features were hierarchically arranged from coarse to fine by exploiting both topological and volumetric representations. The topological representation encoded the intrinsic topology of the body's shape using a skeleton-based structure, thus guaranteeing invariance to scale, rotations and postural changes and achieving a high level of detail with a moderate computational cost. On the other hand, using the volumetric representation features were described in terms of 3D cylindrical histograms working within a wider range of distances in a faster way and also guaranteeing good invariance properties. The discrimination capabilities were evaluated in four different real-home scenarios related with the fields of ambient assisted living and homecare, namely “dangerous event detection”, “anomalous behaviour detection”, “activities recognition” and “natural human–ambient interaction”. For each mentioned scenario, the discrimination capabilities were evaluated in terms of invariance to viewpoint changes, representation capabilities and classification performance, achieving promising results. The two feature representation approaches exhibited complementary characteristics showing high reliability with classification rates greater than 97%. <s> BIB003 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Single Sensor <s> 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 user’s 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 $\sim 84$ % accuracy in the recognition of abnormal gait segments lasting $\sim 5$ s. Prompt detection of gait anomalies could enable early intervention and prevent falls. <s> BIB004
From Figure 5 , it can be seen that the accelerometer is by far the most adopted sensor in the group of single-sensor type paper with a percentage of adoption of about 70%. This is probably due to the low-cost and to the plethora of devices available on the market, most of them with integrated wireless communication system, small size, weight and long-lasting batteries. For example, Cola et al. BIB004 propose a method requiring the use of a single waist-mounted accelerometer to achieve a continuous monitoring of deviation in the gait of elderly people. Among the single sensor approaches, about one fourth of the papers adopts a wide range of sensor technologies, both commercial products (e.g. Kinect, Wii, cameras) and prototypes. For instance, Diraco et al. BIB003 propose a non-invasive technique for posture classification suitable to be used in several in-home scenarios. This procedure exploits 3D point cloud sequences acquired by using a single time-of-flight sensor to classify the posture hierarchy by using a support vector machine (SVM) approach. Conversely, from the cameras proposed in BIB003 , Najavi et al. BIB001 propose a commercial prototype. A more recent technology can also be employed for gait analysis and fall detection. Stone et al. BIB002 monitored a population of aged people (67-97 years old) for a few months using a Microsoft Kinect sensor (Redmond, WA, USA) in order to evaluate fall risk by means of the standard methodologies timed-up-and-go (TUG) time and habitual gait speed (HGS) tests. 