IntelliChair: a non-intrusive sitting posture and sitting activity recognition system

Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.

The issue of data protection and data security in the (pre-Lisbon) EU third pillar

The key functional operability in the pre-Lisbon PJCCM pillar of the EU is the exchange of intelligence and information amongst the law enforcement bodies of the EU. The twin issues of data protection and data security within what was the EU’s third pillar legal framework therefore come to the fore. With the Lisbon Treaty reform of the EU, and the increased role of the Commission in PJCCM policy areas, and the integration of the PJCCM provisions with what have traditionally been the pillar I activities of Frontex, the opportunity for streamlining the data protection and data security provisions of the law enforcement bodies of the post-Lisbon EU arises. This is recognised by the Commission in their drafting of an amending regulation for Frontex , when they say that they would prefer “to return to the question of personal data in the context of the overall strategy for information exchange to be presented later this year and also taking into account the reflection to be carried out on how to further develop cooperation between agencies in the justice and home affairs field as requested by the Stockholm programme.” The focus of the literature published on this topic, has for the most part, been on the data protection provisions in Pillar I, EC. While the focus of research has recently sifted to the previously Pillar III PJCCM provisions on data protection, a more focused analysis of the interlocking issues of data protection and data security needs to be made in the context of the law enforcement bodies, particularly with regard to those which were based in the pre-Lisbon third pillar. This paper will make a contribution to that debate, arguing that a review of both the data protection and security provision post-Lisbon is required, not only in order to reinforce individual rights, but also inter-agency operability in combating cross-border EU crime. The EC’s provisions on data protection, as enshrined by Directive 95/46/EC, do not apply to the legal frameworks covering developments within the third pillar of the EU. Even Council Framework Decision 2008/977/JHA, which is supposed to cover data protection provisions within PJCCM expressly states that its provisions do not apply to “Europol, Eurojust, the Schengen Information System (SIS)” or to the Customs Information System (CIS). In addition, the post Treaty of Prüm provisions covering the sharing of DNA profiles, dactyloscopic data and vehicle registration data pursuant to Council Decision 2008/615/JHA, are not to be covered by the provisions of the 2008 Framework Decision. As stated by Hijmans and Scirocco, the regime is “best defined as a patchwork of data protection regimes”, with “no legal framework which is stable and unequivocal, like Directive 95/46/EC in the First pillar”. Data security issues are also key to the sharing of data in organised crime or counterterrorism situations. This article will critically analyse the current legal framework for data protection and security within the third pillar of the EU.