110 skills found · Page 1 of 4
ghammad / PyActigraphyPython-based open source package for actigraphy data analysis
ani8897 / Human Activity RecognitionClassifying the physical activities performed by a user based on accelerometer and gyroscope sensor data collected by a smartphone in the user’s pocket. The activities to be classified are: Standing, Sitting, Stairsup, StairsDown, Walking and Cycling.
thesimonho / SensormotionPackage for analyzing human motion data (e.g. PA, gait)
Sh0ckFR / Infosec Useful StuffThis repository is my own list of tools / useful stuff for pentest, defensive activities, programming, lockpicking and physical security
chenfeng-huang / Kaggle Silver Medal Solutioun CMI PIUThis project aims to analyze physical activity data from children and adolescents to predict the extent of their problematic internet use. The goal is to develop a model that can help identify early signs of problematic internet use, thereby promoting the development of healthy digital habits.
SOYJUN / Application With Raw IP SocketsOverview For this assignment you will be developing an application that uses raw IP sockets to ‘walk’ around an ordered list of nodes (given as a command line argument at the ‘source’ node, which is the node at which the tour was initiated), in a manner similar to the IP SSRR (Strict Source and Record Route) option. At each node, the application pings the preceding node in the tour. However, unlike the ping code in Stevens, you will be sending the ping ICMP echo request messages through a SOCK_RAW-type PF_PACKET socket and implementing ARP functionality to find the Ethernet address of the target node. Finally, when the ‘walk’ is completed, the group of nodes visited on the tour will exchange multicast messages. Your code will consist of two process modules, a ‘Tour’ application module (which will implement all the functionality outlined above, except for ARP activity) and an ARP module. The following should prove to be useful reference material for the assignment: Sections 21.2, 21.3, 21.6 and 21.10, Chapter 21, on Multicasting. Sections 27.1 to 27.3, Chapter 27, on the IP SSRR option. Sections 28.1 to 28.5, Chapter 28, on raw sockets, the IP_HDRINCL socket option, and ping. Sections 15.5, Chapter 15, on Unix domain SOCK_STREAM sockets. Figure 29.14, p. 807, and the corresponding explanation on p. 806, on filling in an IP header when the IP_HDRINCL socket option is in effect. The Lecture Slides on ARP & RARP (especially Section 4.4, ARP Packet Format, and the Figure 4.3 it includes). The link http://www.pdbuchan.com/rawsock/rawsock.html contains useful code samples that use IP raw sockets and PF_PACKET sockets. Note, in partcular, the code “icmp4_ll.c” in Table 2 for building an echo request sent through a PF_PACKET SOCK_RAW socket. The VMware environment You will be using the same vm1 , . . . . . , vm10 nodes you used for Assignment 3. However, unlike Assignment 3, you should use only interfaces eth0 and their associated IP addresses and ignore the other Ethernet interfaces that nodes have (interfaces eth0 make vm1 , . . . . . , vm10 look as if they belong to the same Ethernet LAN segment IP network 130.245.156.0/24). Note that, apart from the primary IP addresses associated with interfaces eth0, some nodes might also have one or more alias IP addresses associated with their interface eth0. Tour application module specifications The application will create a total of four sockets: two IP raw sockets, a PF_PACKET socket and a UDP socket for multicasting. We shall call the two IP raw sockets the ‘rt ’ (‘route traversal’) and ‘pg ’ (‘ping’) sockets, respectively. The rt socket should have the IP_HDRINCL option set. You will only be receiving ICMP echo reply messages through the pg socket (and not sending echo requests), so it does not matter whether it has the IP_HDRINCL option set or not. The pg socket should have protocol value (i.e., protocol demultiplexing key in the IP header) IPPROTO_ICMP. The rt socket should have a protocol value that identifies the application - i.e., some value other than the IPPROTO_XXXX values in /usr/include/netinet/in.h. However, remember that you will all be running your code using the same root account on the vm1 , . . . . . , vm10 nodes. So if two of you happen to choose the same protocol value and happen to be running on the same vm node at the same time, your applications will receive each other’s IP packets. For that reason, try to choose a protocol value for your rt socket that is likely to be unique to yourself. The PF_PACKET socket should be of type SOCK_RAW (not SOCK_DGRAM). This socket should have a protocol value of ETH_P_IP = 0x0800 (IPv4). The UDP socket for multicasting will be discussed below. Note that, depending on how you choose to bind that socket, you might actually need to have two UDP sockets for multicast communication – see bottom of p. 576, Section 21.10. Your application will, of course, have to be running on every vm node that is included in the tour. When evoking the application on the source node, the user supplies a sequence of vm node names (not IP addresses) to be visited in order. This command line sequence starts with the next node to be visited from the source node (i.e., it does not start with the source node itself). The sequence can include any number of repeated visits to the same node. For example, suppose that the source node is vm3 and the executable is called badr_tour : [root@vm3/root]# badr_tour vm2 vm10 vm4 vm7 vm5 vm2 vm6 vm2 vm9 vm4 vm7 vm2 vm6 vm5 vm1 vm10 vm8 (but note that the tour does not necessarily have to visit every vm node; and the same node should not appear consequentively in the tour list – i.e., the next node on the tour cannot be the current node itself). The application turns the sequence into a list of IP addresses for source routing. It also adds the IP address of the source node itself to the beginning of the list. The list thus produced will be carried as the payload of an IP packet, not as a SSRR option in the packet header. It is our application which will ensure that every node in the sequence is visited in order, not the IP SSRR capability. The source node should also add to the list an IP multicast address and a port number of its choice. It should also join the multicast group at that address and port number on its UDP socket. The TTL for outgoing multicasts should be set to 1. The application then fills in the header of an IP packet, designating itself as the IP source, and the next node to be visited as the IP destination. The packet is sent out on the rt socket. Note that on Linux, all the fields of the packet header must be in network byte order (Stevens, Section 28.3, p. 737, the fourth bullet point). When filling in the packet header, you should explicitly fill in the identification field (recall that, with the IP_HDRINCL socket option, if the identification field is given value 0, then the kernel will set its value). Try to make sure that the value you choose is likely to be unique to yourself (for reasons similar to those explained with respect to the IPPROTO_XXXX in 1. above). When a node receives an IP packet on its rt socket, it should first check that the identification field carries the right value (this implies that you will hard code your choice of identification field value determined in item 2 above in your code). If the identification field value does not check out, the packet is ignored. For a valid packet : Print out a message along the lines of: <time> received source routing packet from <hostname> <time> is the current time in human-readable format (see lines 19 & 20 in Figure 1.9, p. 14, and the corresponding explanation on p. 14f.), and <hostname> is the host name corresponding to the source IP address in the header of the received packet. If this is the first time the node is visited, the application should use the multicast address and port number in the packet received to join the multicast group on its UDP socket. The TTL for outgoing multicasts should be set to 1. The application updates the list in the payload, so that the next node in the tour can easily identify what the next hop from itself will be when it receives the packet. How you do this I leave up to you. You could, for example, include as part of the payload a pointer field into the list of nodes to be visited. This pointer would then be updated to the next entry in the list as the packet progresses hop by hop (see Figure 27.1 and the associated explanation on pp. 711-712). Other solutions are, of course, possible. The application then fills in a new IP header, designating itself as the IP source, and the next node to be visited as the IP destination. The identification field should be set to the same value as in the received packet. The packet is sent out on the rt socket. The node should also initiate pinging to the preceding node in the tour (the IP address of which it should pick up from the header of the received packet). However, unlike the Stevens ping code, it will be using the SOCK_RAW-type PF_PACKET socket of item 1 above to send the ICMP echo request messages. Before it can send echo request messages, the application has to call on the ARP module you will implement to get the Ethernet address of this preceding / ‘target’ node; this call is made using the API function areq which you will also implement (see sections ARP module specifications & API specifications below). Note that ARP has to be evoked every time the application wants to send out an echo request message, and not just the first time. An echo request message has to be encapsulated in a properly-formulated IP packet, which is in turn encapsulated in a properly-formulated Ethernet frame transmitted out through the PF_PACKET socket ; otherwise, ICMP at the source node will not receive it. You will have to modify Stevens’ ping code accordingly, specifically, the send_v4 function. In particular, the Ethernet frame must have a value of ETH_P_IP = 0x0800 (IPv4 – see <linux/if_ether.h>) in the frame type / ‘length’ field ; and the encapsulated IP packet must have a value of IPPROTO_ICMP = 0x01 (ICMPv4 – see <netinet_in.h>) in its protocol field. You should also simplify the ping code in its entirety by stripping all the ‘indirection’ IPv4 / IPv6 dual-operability paraphernalia and making the code work just for IPv4. Also note that the functions host_serv and freeaddrinfo, together with the associated structure addrinfo (see Sections 11.6, 11.8 & 11.11), in Figures 27.3, 27.6 & 28.5 ( pp. 713, 716 & 744f., respectively) can be replaced by the function gethostbyname and associated structure hostent (see Section 11.3) where needed. Also, there is no ‘-v’ verbose option, so this too should be stripped from Stevens’ code. When a node is ready to start pinging, it first prints out a ‘PING’ message similar to lines 32-33 of Figure 28.5, p. 744. It then builds up ICMP echo request messages and sends them to the source node every 1 second through the PF_PACKET socket. It also reads incoming echo response messages off the pg socket, in response to which it prints out the same kind of output as the code of Figure 28.8, p. 748. If this node and its preceding node have been previously visited in that order during the tour, then pinging would have already been initiated from the one to the other in response to the first visit, and nothing further should nor need be done during second and subsequent visits. In light of the above, note that once a node initiates pinging, it needs to read from both its rt and pg sockets, necessitating the use of the select function. As will be clear from what follows below, the application will anyway be needing also to simultaneously monitor its UDP socket for incoming multicast datagrams. When the last node on the tour is reached, and if this is the first time it is visited, it joins the multicast group and starts pinging the preceding node (if it is not already doing so). After a few echo replies are received (five, say), it sends out the multicast message below on its UDP socket (i.e., the node should wait about five seconds before sending the multicast message) : <<<<< This is node vmi . Tour has ended . Group members please identify yourselves. >>>>> where vmi is the name (not IP address) of the node. The node should also print this message out on stdout preceded, on the same line, by the phrase: Node vmi . Sending: <then print out the message sent>. Each node vmj receiving this message should print out the message received preceded, on the same line, by the phrase: Node vmj . Received <then print out the message received>. Each such node in step a above should then immediately stop its pinging activity. The node should then send out the following multicast message: <<<<< Node vmj . I am a member of the group. >>>>> and print out this message preceded, on the same line, by the phrase: Node vmj . Sending: <then print out the message sent>. Each node receiving these second multicast messages (i.e., the messages that nodes – including itself – sent out in step c above) should print each such message out preceded, on the same line, by the phrase: Node vmk . Received: <then print out the message received>. Reading from the socket in step d above should be implemented with a 5-second timeout. When the timeout expires, the node should print out another message to the effect that it is terminating the Tour application, and gracefully exit its Tour process. Note that under Multicast specifications, the last node in the tour, which sends out the End of Tour message, should itself receive a copy of that message and, when it does, it should behave exactly as do the other nodes in steps a. – e. above. ARP module specifications Your executable is evoked with no command line arguments. Like the Tour module, it will be running on every vm node. It uses the get_hw_addrs function of Assignment 3 to explore its node’s interfaces and build a set of <IP address , HW address> matching pairs for all eth0 interface IP addresses (including alias IP addresses, if any). Write out to stdout in some appropriately clear format the address pairs found. The module creates two sockets: a PF_PACKET socket and a Unix domain socket. The PF_PACKET should be of type SOCK_RAW (not type SOCK_DGRAM) with a protocol value of your choice (but not one of the standard values defined in <linux/if_ether.h>) which is, hopefully, unique to yourself. This value effectively becomes the protocol value for your implementation of ARP. Because this protocol value will be carried in the frame type / ‘length’ field of the Ethernet frame header (see Figure 4.3 of the ARP & RARP handout), the value chosen should be not less than 1536 (0x600) so that it is not misinterpreted as the length of an Ethernet 802.3 frame. The Unix domain socket should be of type SOCK_STREAM (not SOCK_DGRAM). It is a listening socket bound to a ‘well-known’ sun_path file. This socket will be used to communicate with the function areq that is implemented in the Tour module (see the section API specifications below). In this context, areq will act as the client and the ARP module as the server. The ARP module then sits in an infinite loop, monitoring these two sockets. As ARP request messages arrive on the PF_PACKET socket, the module processes them, and responds with ARP reply messages as appropriate. The protocol builds a ‘cache’ of matching <IP address , HW address> pairs from the replies (and requests – see below) it receives. For simplicity, and unlike the real ARP, we shall not implement timing out mechanisms for these cache entries. A cache entry has five parts: (i) IP address ; (ii) HW address ; (iii) sll_ifindex (the interface to be used for reaching the matching pair <(i) , (ii)>) ; (iv) sll_hatype ; and (v) a Unix-domain connection-socket descriptor for a connected client (see the section API specifications below for the latter three). When an ARP reply is being entered in the cache, the ARP module uses the socket descriptor in (v) to send a reply to the client, closes the connection socket, and deletes the socket descriptor from the cache entry. Note that, like the real ARP, when an ARP request is received by a node, and if the request pertains to that receiving node, the sender’s (see Figure 4.3 of the ARP & RARP handout) <IP address, HW address> matching pair should be entered into the cache if it is not already there (together, of course, with (iii) sll_ifindex & (iv) sll_hatype), or updated if need be if such an entry already exists in the cache. If the ARP request received does not pertain to the node receiving it, but there is already an entry in that receiving node's cache for the sender’s <IP address, HW address> matching pair, that entry should be checked and updated if need be. If there is no such entry, no action is taken (in particular, and unlike the case above, no new entry should be made in the receiving node's cache of the sender’s <IP address, HW address> matching pair if such an entry does not already exist). ARP request and reply messages have the same format as Figure 4.3 of the ARP & RARP handout, but with an extra 2-byte identification field added at the beginning which you fill with a value chosen so that it has a high probability of being unique to yourself. This value is to be echoed in the reply message, and helps to act as a further filter in case some other student happens to have fortuitously chosen the same value as yourself for the protocol parameter of the ARP PF_PACKET. Values in the fields of our ARP messages must be in network byte order. You might find the system header file <linux/if_arp.h> useful for manipulating ARP request and reply messages, but remember that our version of these messages have an extra two-byte field as mentioned above. Your code should print out on stdout, in some appropriately clear format, the contents of the Ethernet frame header and ARP request message you send. As described in Section 4.4 of the ARP & RARP handout, the node that responds to the request should, in its reply message, swap the two sender addresses with the two target addresses, as well as, of course, echo back the extra identification field sent with the request. The protocol at this responding node should print out, in an appropriately clear format, both the request frame (header and ARP message) it receives and the reply frame it sends. Similarly, the node that sent the request should print out the reply frame it receives. Finally, recall that the node issuing the request sends out a broadcast Ethernet frame, but the responding node replies with a unicast frame. API specifications The API is for communication between the Tour process and the ARP process. It consists of a single function, areq, implemented in the Tour module. areq is called by send_v4 function of the application every time the latter want to send out an ICMP echo request message: int areq (struct sockaddr *IPaddr, socklen_t sockaddrlen, struct hwaddr *HWaddr); IPaddr contains the primary or alias IPaddress of a ‘target’ node on the LAN for which the corresponding hardware address is being requested. hwaddr is a new structure (and not a pre-existing type) modeled on the sockaddr_ll of PF_PACKET; you will have to declare it in your code. It is used to return the requested hardware address to the caller of areq : structure hwaddr { int sll_ifindex; /* Interface number */ unsigned short sll_hatype; /* Hardware type */ unsigned char sll_halen; /* Length of address */ unsigned char sll_addr[8]; /* Physical layer address */ }; areq creates a Unix domain socket of type SOCK_STREAM and connects to the ‘well-known’ sun_path file of the ARP listening socket. It sends the IP address from parameter IPaddr and the information in the three fields of parameter HWaddr to ARP. It then blocks on a read awaiting a reply from ARP. This read should be backed up by a timeout since it is possible that no reply is received for the request. If a timeout occurs, areq should close the socket and return to its caller indicating failure (through its int return value). Your application code should print out on stdout, in some appropriately clear format, a notification every time areq is called, giving the IP address for which a HW address is being sought. It should similarly print out the result when the call to areq returns (HW address returned, or failure). When the ARP module receives a request for a HW address from areq through its Unix domain listening socket, it first checks if the required HW address is already in the cache. If so, it can respond immediately to the areq and close the Unix domain connection socket. Else : it makes an ‘incomplete’ entry in the cache, consisting of parts (i), (iii), (iv) and (v) ; puts out an ARP request message on the network on its PF_PACKET socket; and starts monitoring the areq connection socket for readability – if the areq client closes the connection socket (this would occur in response to a timeout in areq), ARP deletes the corresponding incomplete entry from the cache (and ignores any subsequent ARP reply from the network if such is received). On the other hand, if ARP receives a reply from the network, it updates the incomplete cache entry, responds to areq, and closes the connection socket.
BiswajitPadhi99 / Predicting Cloud CPU Utilization On Azure Dataset Using DeeplearningMany companies are utilizing the cloud for their day to day activities. Many big cloud service providers like AWS, Microsoft Azure have been success-fully serving its increasing customer base. A brief understanding of the char-acteristics of production virtual machine (VM) workloads of large cloud pro-viders can inform the providers resource management systems, e.g. VM scheduler, power manager, server health manager. In our project we will be analysing Microsoft Azure’s VM CPU utilization dataset released in October 2017. We predict the VM workload from the CPU usage pattern like mini-mum, maximum and average from the Azure dataset. Different techniques among Deep learning are used for the prediction by considering the history of the workload. By considering real VM traces, we can show that the predic-tion-informed schedules increase utilization and stop physical resource ex-haustion. We can arrive at a conclusion that cloud service providers can use their workloads’ characteristics and machine learning techniques to enhance resource management greatly.
abdallahkhairy / GP Data Analysis And MLHuman locomotion affects our daily living activities. Losing limbs or having neurological disorders with motor deficits could affect the quality of life. Gait analysis is a systematic study of human locomotion, which is defined as body movements through aerial, aquatic, or terrestrial space. This analysis has been used to study people ambulation, registration, and reconstruction of physical location and orientation of individual limbs used to quantify and characterize human locomotion using different gait parameters including gait activities such as walking, stairs ascending/descending, … etc., phases, and spatiotemporal parameters of human gait. Additionally, gait analysis parameters can be used to evaluate the functionality of patients and wearable system users. The evaluation is based on patient's stability, energy consumption, gait symmetry, ability to recover from perturbations, and ability to perform activities of daily living. Many companies develop assistive, wearable, and rehabilitation devices for patients with lower limb neurological disorders. These devices are tested and evaluated inside controlled lab environments. However, they don’t have enough data on the patient's performance in real world and harsh environments. Collecting large datasets of device users and their gait performance data in real environment are notoriously difficult. Additionally, collecting data on less prevalent or on gait activities other than level walking, stair ascending/descending, sitting, standing, …etc. on hard surfaces is rarely attempted. However, the scope for collecting gait data from alternative sources other than traditional gait labs could be attained with the help of IoT data collection embedded on the wearable and assistive devices and well-established cloud platforms equipped with big-data analytics and data visualization capabilities. This project aims to develop a cloud platform capable of collect data from wearable and assistive devices such as prostheses, exoskeleton, gait analysis wearable sensors, …etc. using IoT technologies. This platform is capable of automatically use data mining and visualization tools. Additionally, it uses statistical and machine learning techniques to estimate gait events, gait symmetry, gait speed, gait activities, stability, energy consumption, …etc. Also, it is capable of predicting patient's progress over time. The project will be composed of two major components, hardware component and software component. In hardware component, the students will design and implement the IoT that collects the different readings for gait analysis and send them to the cloud. Meanwhile, in the software component, the students will design and implement a set of algorithms to visualize the collected data, then design and implement data analytics to automatically analyze the collected data, so that we can estimate gait events, gait symmetry, gait speed, classify gait activities, stability, energy consumption, …etc. and predicting patient's progress over time. By analyzing the collected data, the patient's progress can be predicted over time. Additionally, these data can be used through manufacturers of prostheses legs to improve their products, as well as through health-care centers to assess the patient's performance. The following figures describe the main modules of our graduation project.
Thomite / PamproPhysical Activity Monitor Processing
ease-crc / SomaThe Socio-physical Model of Activities (SOMA) is a formal activity model for embodied agents that need to operate their body to generate motions that cause intentional effects in the physical and social world.
yc244 / Stata Code BMC Medince2019 ChudasamaPhysical activity, multimorbidity, and life expectancy: a UK Biobank longitudinal study
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andreasKyratzis / PAMAP2 Physical Activity Monitoring Data Analysis And MLData Analysis and Machine Learning on Physical monitoring data
BernardNyarko / Smart Monitor An AI Powered IoT Monitoring System For Small Medium Scale PremisesWith recent advances in both Artificial Intelligence (AI) and Internet of Things (IoT) capabilities, it is more possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time. Imagine a surveillance camera system that can detect various on-body weapons, suspicious objects, and traffic. This system could transform surveillance cameras from passive sentries into active observers, which would help prevent a possible mass shooting in a school, stadium, or mall. In this project, we tried to realize such systems by implementing Smart-Monitor, an AI-powered threat detector for intelligent surveillance cameras. The developed system can be deployed locally on the surveillance cameras at the network edge. Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images on-site, reducing the communication overheads and enabling swift security actions. We developed a mobile app that users can detect suspicious objects in an image and video captured by several cameras at the network edge. Also, the model can generate a high-quality segmentation mask for each object instance in the photo, along with the confidence percentage. The camera side used a Raspberry Pi 4 device, Neural Compute Stick 2 (NCS 2), Logitech C920 webcam, motion sensors, buzzers, pushbuttons, LED lights, Python Face recognition, and TensorFlow Custom Object Detection. When the system detects a motion in the surrounding environment, the motion sensors send a signal to the Raspberry Pi device notifying it to start capturing images for such physical activity. Using Python’s face recognition and TensorFlow 2 custom object detection Smart-Monitor can recognize eight classes, including a baseball bat, bird, cat, dog, gun, hammer, knife, and human faces. Finally, we evaluated our system using various performance metrics such as classification time and accuracy, scalability, etc.
strudelauxpommes / FitnessHabitsThis Android software tracks fitness activities and health habits such as physical activities, fluids intake, supplements, food intake, sleep durations, and alcool intake.
jocelynZXY / RadarDataforCSBHRDRadar Data for Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars
Ytemiloluwa / SportsAppSportsApp in SwiftUI (Monitoring physical activities with charts)
Ergo-Tools / ActiPASSThe thigh-worn accelerometer data processing tool to find physical and sedentary behaviour
StairMasterClimber / MobileThe Stair Climbing Revolution that blends fitness and fun! Challenge friends to perform physical activity and conquer the leaderboard!
Project-PetCare / Petcare AppAn android application to track pets' health, veterinarian visits and physical activity. This was created for course Mobile Development Project at Oulu University of Applied Sciences.
