Pervasive Computing for Assisting Individuals With Autism - Assignment Example

Employee Performance Evaluation Pervasive Computing for Assisting Individuals with Autism Background Check and Requirement Analysis Providing care to individuals and children with autism is mostly a complicated lifelong challenge. There are numerous options on the types of care that are currently available for care networks and families of children with autism. These include various therapies, diet or drug regimens, and doctors’ visits. Due to the exclusive life of each youngster, it is difficult to determine the types of treatments that are suitable, and time is usually a critical factor in the administration of treatments. These complexities may force parents or caregivers to administer several treatments at the same time, thus increasing the difficulties in establishing the types of treatments that are versatile and those that do not work. Moreover, complications arise when the outcomes of the treatments are not evident immediately or may make minimal imperceptible changes (Howlin et al, 2004). Computing technology is capable of helping caregivers in the management of health and educational needs of autistic children. Some of the pervasive computing technologies are designed to assist in collecting records, analysing records, making decisions, and communicating (Kientz et al, 2008). Three computing technologies are currently available for helping individuals with autism and their caregivers. They include the Discrete Trial Training therapy (DTT) (it supports collaborative decision making), CareLog (to capture and analyse behavioural data), and the use of wearable sensors to give nonverbal autistic children a voice (Kientz et al, 2007). One of the fundamental challenges of providing care to some autistic children is the children’s inability to communicate in case they feel uncomfortable or upset about something (Kientz et al, 2007). The spotlight of this assignment is to investigate a pervasive computing technology that focuses on providing nonverbal autistic children with a voice. In other words, the technology helps such autistic children to communicate using wearable sensors. In order to obtain information about the target group and their needs detailed contextual inquiries in the field of autism shall be conducted. The explorations include interviews with caregivers, parents, teachers who interact with the children, and therapists. The information gathering process also includes participating in therapy sessions as well as meeting teams of therapists. Extensive time shall be spent observing home settings, classrooms, support groups, various types of therapy sessions, and other activities within the autism domain. The design team shall also include people who have expert knowledge of the user population, including autism researchers, behavioural specialists, and parents of autistic children. The design team will be better attuned to how appropriate the target population would adopt the technologies if the team gains first-hand experience of children with autism (Westeyn, 2005). Because autistic children often cannot express their needs, it is important to ensure that the data collection process meets their best interests. Similarly, teachers, caregivers, and the professionals who are involved have immediate concerns about data collection that may affect their work or have legal consequences, especially if video recordings are taken out of context. For instance, restraining a child may provide some comfort, but it may look like abuse to an uninformed outside viewer. Due to all the data collection requirements, some technologies may violate the caregivers’ or child’s privacy if it is not considered carefully (Kientz et al, 2008). Besides these challenges, coordination among different people includes issues related to security, privacy, and legislative control. For instance, there are certain Acts of legislation that protect patients. These policies are designed to certify that educational and medical data are kept private shielded from malevolence, and the software that is designed to discuss and share the data is not used inappropriately. The design of applications that conform to these regulations will also assist in addressing any concerns that may be raised by a person receiving care or the caregivers that are close to the individual (Kientz et al, 2008). Problem Specification A fundamental problem in providing care for some autistic children lies in their lack of communication skills. Some children with autism are not able to communicate when something makes them uncomfortable or something upsets them (Kientz et al, 2007). Pervasive computing such as wearable sensors can help caregivers know when the child is feeling upset or uncomfortable. A defining attribute of autistic children is self-stimulatory behaviour, also known as stimming. The behaviour includes hand waving, rocking, clenching fists, and other non-verbal vocalizations (Kientz, 2006). According to some researchers, the frequency of such behaviours is an indication of the child’s affective state. By monitoring stimming behaviour, caregivers might gain a better idea of the effects of treatments when attempting to sooth or calm the child. It may also offer caregivers a better idea of when a child is more willing to learn (Kientz et al, 2008). Sensing Solution: Wearable Sensors This project sought to research technological ways for detecting stimming or self-stimulatory behaviours by wearable wireless sensors. The team created a proof-of-concept system that is able to collect data from an autistic child and provide automatic indices into the data to assist researchers when analysing autism. The pilot study faced various technical challenges with respect to selecting and placing the sensors and recognizing the collected data. Wireless Bluetooth accelerometers were used to particularly target self-stimulatory behaviours of repetitive movements since they are usually the most disruptive socially. The positioning of accelerometers is a significant consideration for the project. For instance, if a child exhibits repetitive flapping of the hands, positioning the accelerometer on the wrist would be more appropriate than positioning it on the ankle. The aim here is to reduce the number of sensors required to collect data for as many self-stimulatory events as possible (Hayes, 2005). During the testing stage, an adult mimicked various trials of seven stimming behaviours in-between random daily activities. Three accelerometers were affixed on the subject’s wrist, waist, and thigh using straps. Project Planning The project implementation shall take a period of fifteen months. The first three months shall involve planning activities. Eight months shall involve obtaining information about the target group and their needs and conducting detailed contextual inquiries in the field of autism. The explorations shall include interviews with caregivers, parents, teachers who interact with the children, and therapists. Extensive time shall also be spent observing home settings, classrooms, support groups, various types of therapy sessions, and other activities within the autism domain. The remaining four months will involve developing and testing of the sensors and other hardware necessary for the project. This period will also be used to source for an appropriate Gesture Toolkit for recognition experiments, and software for analysing the data that I shall collect. The project management approach that will be employed in this project is a traditional five stage process. The stages include initiation, planning and design, executing, monitoring and control, and closing. This approach was selected because it is the most basic project management approach. Wysocki (2011) posits that all the five stages in this approach are performed in sequence, and the extension of reclusiveness of some processes for supporting the development cycle. The initiation stage involves determining that scope and nature of the project. This stage also involves determining the primary elements of the project such as establishing goals, conducting a requirement analysis and a feasibility review. The planning and design stage involves planning for time, costs, and resources as the project is running. The execution stage consists of activities that are used in the processes for working in synchronization with the plan of the project in order to meet deadlines for each milestone (Wysocki, 2011). The executing stage involves conducting the interviews with therapists, parents, caregivers, and other professionals within the autism domain. It also involves observing and recording videos of autistic children in the classroom setting, at home, or in care homes. In addition, the stage involves sourcing, developing, and testing the required hardware and software. Monitoring and control involve processes that validate and verify the activities and the sequence of the activities according to timelines of the project plan. According to Wysocki (2011), this stage also involves considering the quality of activities. For instance, this is where we consider the quality of the data collected and the appropriateness of data collection methods. The closing stage involves formal acceptance of the outcomes of the project. Adhering to the timelines, budgets and goals of the project plan will ensure that the project stays on track. The project plan lays out each step of the development process and it defines the plan of action that will be followed even in conditions of risk. The methodologies of the project plan also aid in tracking down the success rate of each activity in the project. The project shall consider outsourcing expert advice and technical assistance from experts in computer engineering and experts in the autism domain. These experts will offer valuable insights into various aspects of the project that may present unforeseen challenges. The possible lack of sufficient financial resources that are required to implement the project successfully is another challenge. The project shall ensure that all costs are managed in the best possible way to keep them minimal. Some major challenges in developing wearable sensing systems include the type of sensor, the power it consumes, and its form. The type and location of sensors will have a consequence on the eminence of data collected. Sensors that need more power need to have large batteries, which makes the sensors heat dissipating and heavy. The use of smaller batteries that are lighter is a solution to the challenge, but these will need more frequent charging and may burden caregivers. Concerning the form of sensors, the sensors should remain in position when in use, be unobtrusive, and have protection from wear. Maintaining the sensors in the appropriate position may be challenging for unwilling subjects. A child can damage or remove sensors on clothing or bracelets. A solution may be to use environmental sensors along with appropriately concealed on-body sensors (Kientz, 2007). Evaluation The success of this project is measurable on two factors. The first factor is whether the wearable sensors are able to generate meaningful data about certain self-stimulatory behaviours. Secondly, success is measurable on the technology’s ability to perform automatic indexing into the available data. The system would be successful if the technology can accurately detect that a self-stimulatory behaviour has occurred and afford us the opportunity of labelling the kind of behaviour correctly. The users of the product of this project have a responsibility to take part in in the success of this project. In particular, the targeted users of the sensor system are children with autism. The role of the users here is to evaluate whether the system is safe and convenient for autistic children of varying ages. Reference List Hayes, G.R., Truong, K.N., Abowd, G.D., Pering. T., 2005, ‘Experience Buffers: A Socially Appropriate, Selective Archiving Tool for Evidence-Based Care’, In CHI’05 Extended Abstracts on Human Factors in Computing Systems, ACM Press, pp. 1435-1438. Howlin, P., Goode, S., Hutton, J., and Rutter, M., 2004, ‘Adult outcome for children with autism. The Journal of Child Psychology and Psychiatry’, Vol. 45, No. 2, pp. 212-339. Kientz, J.A., Hayes G.R., Abowd, G.D., and Grinter, R.E., 2006, ‘From the War Room to the Living Room: Decision Support for Home-based Therapy Teams’, In the Proc. of CSCW ’06, ACM Press, pp. 209-218. Kientz, J.A., Arriaga, R.I., Hayes, G.R., and Abowd, G.D., 2008, ‘Designing and Developing Technology for Caregivers of Individuals with Autism’, CHI 2008 Workshop on Technology in Mental Health, pp. 8-13. Kientz, J.A., Hayes, G.R., Westeyn, T., Starner, T., and Abowd, G.D., 2007, ‘Pervasive Computing and Autism: Assisting Caregivers of Children with Special Needs’, In Healthcare, IEEE Computer Society, pp. 28-35. Westeyn, T., 2005, ‘Recognizing Mimicked Autistic Self Stimilatory Behaviors Using HMMs,’ Proc 9th IEEE Int’l Symp, Wearable Computers (ISWC 05), IEEE CS Press, pp. 164-167. Wysocki, R.K., 2011, ‘Effective Project Management: Traditional, Agile, Extreme,’ John Wiley & Sons, New York. Read More