UWB and WiFi Systems as Passive Opportunistic Activity Sensing Radars

Human Activity Recognition (HAR) is becoming increasingly important in smart homes and healthcare applications such as assisted-living and remote health monitoring. In this paper, we use Ultra-Wideband (UWB) and commodity WiFi systems for the passive sensing of human activities. These systems are based on a receiver-only radar network that detects reflections of ambient Radio-Frequency (RF) signals from humans in the form of Channel Impulse Response (CIR) and Channel State Information (CSI). An experiment was performed whereby the transmitter and receiver were separated by a fixed distance in a Line-of-Sight (LoS) setting. Five activities were performed in between them, namely, sitting, standing, lying down, standing from the floor and walking. We use the high-resolution CIRs provided by the UWB modules as features in machine and deep learning algorithms for classifying the activities. Experimental results show that a classification performance with an F1-score as high as 95.53% is achieved using processed UWB CIR data as features. Furthermore, we analysed the classification performance in the same physical layout using CSI data extracted from a dedicated WiFi Network Interface Card (NIC). In this case, maximum F1-scores of 92.24% and 80.89% are obtained when amplitude CSI data and spectrograms are used as features, respectively

UWB and WiFi Systems as Passive Opportunistic Activity Sensing Radars

Human Activity Recognition (HAR) is becoming increasingly important in smart homes and healthcare applications such as assisted-living and remote health monitoring. In this paper, we use Ultra-Wideband (UWB) and commodity WiFi systems for the passive sensing of human activities. These systems are based on a receiver-only radar network that detects reflections of ambient Radio-Frequency (RF) signals from humans in the form of Channel Impulse Response (CIR) and Channel State Information (CSI). An experiment was performed whereby the transmitter and receiver were separated by a fixed distance in a Line-of-Sight (LoS) setting. Five activities were performed in between them, namely, sitting, standing, lying down, standing from the floor and walking. We use the high-resolution CIRs provided by the UWB modules as features in machine and deep learning algorithms for classifying the activities. Experimental results show that a classification performance with an F1-score as high as 95.53% is achieved using processed UWB CIR data as features. Furthermore, we analysed the classification performance in the same physical layout using CSI data extracted from a dedicated WiFi Network Interface Card (NIC). In this case, maximum F1-scores of 92.24% and 80.89% are obtained when amplitude CSI data and spectrograms are used as features, respectively

The colour of life: interacting with SenseCam images on large multi-touch display walls

A SenseCam can provide a detailed visual archive of a person’s life, activities and experiences. However, as the number of images captured per year can extend beyond one million, gaining an insight into an individual’s lifestyle in a fast, effective and intuitive manner is a challenging prospect. In this work, we develop an interactive image browsing tool, which incorporates visualisation techniques that can capture not only a snapshot of an individual’s lifestyle over long periods of time, but also how that lifestyle varies with changing days, weeks, or years. The image retrieval tool incorporates the Colour of Life algorithms [1], which can represent an overview of millions of images with a single visualisation. The Colour of Life algorithms focus on the relationship between lifestyle and colour, by capturing the colours to which we are exposed in our lives (and therefore captured by SenseCam images), collating similar colours for specific time periods and depicting how those colours change over time with a flowing time-line – see Figure 1 which depicts the life of a SenseCam user over the period of 8 days. In this figure, time is orientated along the horizontal axis and larger vertical peaks indicate higher user activity for a given period of time. In Figure 1, the normal working week consists of the rhythmical blue, pink (work) and yellow (home) peaks and troughs for each day (with less activity at the start and end of the days), whereas time outdoors increases at the weekend, especially during the night (and hence the darker colours on the left hand side of the figure). The Colour of Life visualisation, while providing information on changes in lifestyle, does not provide sufficient context to understand the exact activities of a user for a given time period. For example, on the left of Figure 1 there is a peak of purple, that does not occur anywhere else during the 8 days of activities images – where was the user at this point in time and what was he doing? In this work, we build an interactive image browsing tool based around the Colour of Life visualisation. We exploit the use of high resolution multi-touch display walls, where we extend the Colour of Life algorithms to produce an intuitive visualisation, which incorporates image mosaicing (see Figure 2). Through this we incorporate coarse lifestyle data with more fine detailed contextual information on human activities into one interactive visualisation tool. As an additional feature, we have investigated the use of image classification within the framework of the Colour of Life. One such example is the categorisation of images as being as social (i.e. interacting with other people) or non-social. Using such a classification, we can depict a person’s social lifestyle, and how that varies over time

Novel Time Domain Based Upper-Limb Prosthesis Control using Incremental Learning Approach

The upper limb of the body is a vital for various kind of activities for human. The complete or partial loss of the upper limb would lead to a significant impact on daily activities of the amputees. EMG carries important information of human physique which helps to decode the various functionalities of human arm. EMG signal based bionics and prosthesis have gained huge research attention over the past decade. Conventional EMG-PR based prosthesis struggles to give accurate performance due to off-line training used and incapability to compensate for electrode position shift and change in arm position. This work proposes online training and incremental learning based system for upper limb prosthetic application. This system consists of ADS1298 as AFE (analog front end) and a 32 bit arm cortex-m4 processor for DSP (digital signal processing). The system has been tested for both intact and amputated subjects. Time derivative moment based features have been implemented and utilized for effective pattern classification. Initially, system have been trained for four classes using the on-line training process later on the number of classes have been incremented on user demand till eleven, and system performance has been evaluated. The system yielded a completion rate of 100% for healthy and amputated subjects when four motions have been considered. Further 94.33% and 92% completion rate have been showcased by the system when the number of classes increased to eleven for healthy and amputees respectively. The motion efficacy test is also evaluated for all the subjects. The highest efficacy rate of 91.23% and 88.64% are observed for intact and amputated subjects respectively.Comment: 15 Pages, 8 Figures, This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

KOMPOSISI SAMPAH LAUT (MARINE DEBRIS) DI KAWASAN PESISIR BARAT PANTAI AMPENAN KOTA MATARAM

Marine debris is a material wasted intentionally or unintentionally into coastal and marine environment caused by human activities. Trash has become one of the biggest contributors to coastal and marine pollution that may disrupt the balance of aquatic ecosystem, reduce the aesthetic value of coastal and marine areas, and even disturb human health. This research was conducted to provide information regarding type, weight, and source of trash found around western coastal area of Ampenan Beach, Mataram City. Sampling spots which represented a condition of study area were determined using purposive sampling. Two research locations were chosen around Ampenan Beach, namely Penghulu Agung Beach and estuary of Jangkok River. Trash collection was done done using line transect method based on criteria determined from length of the coastline. The trash collected was then sorted, weighed, and identified based on trash classification system. Marine trash found around the research locations, both at Penghulu Agung Beach (Stasion 1) and at estuary of Jangkok River (Station 2), was dominated by plastics with an average number of trash pieces, respectively, of 7 item/m2 and 9 items/m2. Meanwhile, the weights of plastic trash found at station 1 and 2, respectively, were 29,76 gr/m2 and 213 gr/m2. The amount of plastic trash found at research locations might be the impact of human activities throwing the trash into the estuary and the beach

Chapter Mapping and factoring the 2007 ATECO categories in regard to specialised human capital

The paper describes an exercise of classification of a subset of five-digit categories of the 2007 ATECO classification system of economic activities. The analysis is grounded on the hypothesis that economic sectors can be clustered according to the competency level required to human resources recently working in industries or services in Italy. The analysis may be useful to evaluate a possible relationship between economic development and education. The analysis consisted of a mapping and then a clustering of the Ateco categories according to the between-distribution dissimilarity of any possible couple of categories. The basic idea was to highlight the Ateco categories that require either more education than others or more education and working experience (human capital) than others, pinpointing, in particular, the categories that require larger percentages of tertiary education and those residing close to territorial hubs. The competency level was measured with a combination of educational attainment and in-service experience of Italian employees, as defined by Istat, the Italian statistical institute. The employees’ educational level was evaluated with the frequency distribution of five (ordinal) classes of education of people employed in 2018 and 2019 in both private and public establishments and offices; the working experience with a logarithmic transform of the average number of in-service years of employees. The analysis highlighted both a sort of input-related classification of the economy and a supply-side classification of the labour market. The results are in line with the theory of the existence of a cluster of creative companies residing close to territorial hubs

Towards a Hybrid Method to Categorize Interruptions and Activities in Healthcare

Objective Interruptions are known to have a negative impact on activity performance. Understanding how an interruption contributes to human error is limited because there is not a standard method for analyzing and classifying interruptions. Qualitative data are typically analyzed by either a deductive or an inductive method. Both methods have limitations. In this paper a hybrid method was developed that integrates deductive and inductive methods for the categorization of activities and interruptions recorded during an ethnographic study of physicians and registered nurses in a Level One Trauma Center. Understanding the effects of interruptions is important for designing and evaluating informatics tools in particular and for improving healthcare quality and patient safety in general. Method The hybrid method was developed using a deductive a priori classification framework with the provision of adding new categories discovered inductively in the data. The inductive process utilized line-by-line coding and constant comparison as stated in Grounded Theory. Results The categories of activities and interruptions were organized into a three-tiered hierarchy of activity. Validity and reliability of the categories were tested by categorizing a medical error case external to the study. No new categories of interruptions were identified during analysis of the medical error case. Conclusions Findings from this study provide evidence that the hybrid model of categorization is more complete than either a deductive or an inductive method alone. The hybrid method developed in this study provides the methodical support for understanding, analyzing, and managing interruptions and workflow

Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition.

International audienceA tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be true or false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize running, jumping, falling and standing-up actions as well as high jump, pole vault, triple jump and {long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided